WGCNA GO Analysis
Kristen Brown, modified ZD
1/29/2024
WGCNA GO Analysis
sessionInfo() #provides list of loaded packages and version of R. ## R version 4.3.2 (2023-10-31)
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## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
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## Gu, Z. circlize implements and enhances circular visualization
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## If you use it in published research, please cite:
## Gu, Z. simplifyEnrichment: an R/Bioconductor package for Clustering and
## Visualizing Functional Enrichment Results, Genomics, Proteomics &
## Bioinformatics 2022.
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## ========================================Load in data
library(rtracklayer)
gff<-rtracklayer::import("../../data/Pocillopora_acuta_HIv2.genes_fixed.gff3")
gff<-as.data.frame(gff) %>% dplyr::select(-Parent)
dim(gff) # 478988 9## [1] 478988 12names(gff) ## [1] "seqnames" "start" "end" "width"
## [5] "strand" "source" "type" "score"
## [9] "phase" "ID" "transcript_id" "gene_id"transcripts <- subset(gff, type == "transcript")
transcripts_gr <- makeGRangesFromDataFrame(transcripts, keep.extra.columns=TRUE) #extract length information
transcript_lengths <- width(transcripts_gr) #isolate length of each gene
seqnames<-transcripts_gr$ID #extract list of gene id
lengths<-cbind(seqnames, transcript_lengths)
lengths<-as.data.frame(lengths) #convert to data frame
dim(transcripts) #33730 13## [1] 33730 12kegg <- read.delim("../../data/Pocillopora_acuta_HIv2.genes.KEGG_results.txt",header = FALSE)
kegg <- as.data.frame(kegg)
colnames(kegg)[1] <- "gene_id"
colnames(kegg)[2] <- "KEGG_new"
head(kegg)## gene_id KEGG_new
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a
## 2 Pocillopora_acuta_HIv2___RNAseq.g24143.t1b K22584
## 3 Pocillopora_acuta_HIv2___RNAseq.g22918.t1
## 4 Pocillopora_acuta_HIv2___RNAseq.g18333.t1 K03386
## 5 Pocillopora_acuta_HIv2___RNAseq.g7985.t1
## 6 Pocillopora_acuta_HIv2___RNAseq.g13511.t1eggnog<-read.delim("../../data/Pocillopora_acuta_HIv2.genes.EggNog_results.txt")#this file contains all of the go terms, descriptions, kegg, etc
eggnog<- plyr::rename(eggnog, c("X.query"="gene_id"))
head(eggnog,2)## gene_id seed_ortholog evalue score
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a 45351.EDO48725 2.16e-120 364
## 2 Pocillopora_acuta_HIv2___RNAseq.g18333.t1 45351.EDO38694 3.18e-123 355
## eggNOG_OGs
## 1 COG0620@1|root,KOG2263@2759|Eukaryota,38GZS@33154|Opisthokonta,3BNKS@33208|Metazoa
## 2 COG0450@1|root,KOG0852@2759|Eukaryota,38B9P@33154|Opisthokonta,3BGS4@33208|Metazoa
## max_annot_lvl COG_category
## 1 33208|Metazoa E
## 2 33208|Metazoa O
## Description Preferred_name
## 1 Cobalamin-independent synthase, Catalytic domain -
## 2 negative regulation of male germ cell proliferation PRDX4
## GOs
## 1 -
## 2 GO:0000003,GO:0001775,GO:0002252,GO:0002263,GO:0002274,GO:0002275,GO:0002283,GO:0002366,GO:0002376,GO:0002443,GO:0002444,GO:0002446,GO:0003006,GO:0003674,GO:0003824,GO:0004601,GO:0005488,GO:0005515,GO:0005575,GO:0005576,GO:0005615,GO:0005622,GO:0005623,GO:0005737,GO:0005783,GO:0005790,GO:0005829,GO:0006082,GO:0006457,GO:0006464,GO:0006468,GO:0006520,GO:0006575,GO:0006793,GO:0006796,GO:0006807,GO:0006810,GO:0006887,GO:0006915,GO:0006950,GO:0006952,GO:0006955,GO:0006979,GO:0007154,GO:0007165,GO:0007249,GO:0007252,GO:0007275,GO:0007276,GO:0007283,GO:0007548,GO:0008150,GO:0008152,GO:0008219,GO:0008285,GO:0008379,GO:0008406,GO:0008584,GO:0009056,GO:0009266,GO:0009409,GO:0009605,GO:0009607,GO:0009617,GO:0009628,GO:0009636,GO:0009893,GO:0009966,GO:0009967,GO:0009987,GO:0010467,GO:0010604,GO:0010646,GO:0010647,GO:0010941,GO:0010942,GO:0010950,GO:0010952,GO:0012501,GO:0012505,GO:0016043,GO:0016192,GO:0016209,GO:0016310,GO:0016491,GO:0016684,GO:0016999,GO:0017001,GO:0017144,GO:0019222,GO:0019471,GO:0019538,GO:0019725,GO:0019752,GO:0019953,GO:0022414,GO:0022417,GO:0023051,GO:0023052,GO:0023056,GO:0030141,GO:0030162,GO:0030198,GO:0031323,GO:0031325,GO:0031410,GO:0031974,GO:0031982,GO:0031983,GO:0032268,GO:0032270,GO:0032501,GO:0032502,GO:0032504,GO:0032940,GO:0033554,GO:0034774,GO:0035556,GO:0036211,GO:0036230,GO:0042119,GO:0042127,GO:0042221,GO:0042592,GO:0042737,GO:0042742,GO:0042743,GO:0042744,GO:0042802,GO:0042803,GO:0042981,GO:0043062,GO:0043065,GO:0043067,GO:0043068,GO:0043085,GO:0043170,GO:0043207,GO:0043226,GO:0043227,GO:0043229,GO:0043231,GO:0043233,GO:0043280,GO:0043281,GO:0043299,GO:0043312,GO:0043412,GO:0043436,GO:0043900,GO:0043901,GO:0044093,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044267,GO:0044281,GO:0044421,GO:0044422,GO:0044424,GO:0044433,GO:0044444,GO:0044446,GO:0044464,GO:0044703,GO:0045055,GO:0045137,GO:0045321,GO:0045454,GO:0045862,GO:0046425,GO:0046427,GO:0046546,GO:0046661,GO:0046903,GO:0046983,GO:0048232,GO:0048513,GO:0048518,GO:0048519,GO:0048522,GO:0048523,GO:0048583,GO:0048584,GO:0048608,GO:0048609,GO:0048731,GO:0048856,GO:0050789,GO:0050790,GO:0050794,GO:0050896,GO:0051171,GO:0051173,GO:0051179,GO:0051186,GO:0051187,GO:0051234,GO:0051239,GO:0051241,GO:0051246,GO:0051247,GO:0051336,GO:0051345,GO:0051604,GO:0051704,GO:0051707,GO:0051716,GO:0051920,GO:0052547,GO:0052548,GO:0055114,GO:0060205,GO:0060255,GO:0061458,GO:0065007,GO:0065008,GO:0065009,GO:0070013,GO:0070417,GO:0070887,GO:0071704,GO:0071840,GO:0072593,GO:0080090,GO:0097190,GO:0097237,GO:0097708,GO:0098542,GO:0098754,GO:0098869,GO:0099503,GO:0101002,GO:1901564,GO:1901605,GO:1902531,GO:1902533,GO:1904813,GO:1904892,GO:1904894,GO:1905936,GO:1905937,GO:1990748,GO:2000116,GO:2000241,GO:2000242,GO:2000254,GO:2000255,GO:2001056,GO:2001233,GO:2001235,GO:2001267,GO:2001269
## EC KEGG_ko
## 1 2.1.1.14 ko:K00549
## 2 1.11.1.15 ko:K03386
## KEGG_Pathway
## 1 ko00270,ko00450,ko01100,ko01110,ko01230,map00270,map00450,map01100,map01110,map01230
## 2 ko04214,map04214
## KEGG_Module KEGG_Reaction KEGG_rclass
## 1 M00017 R04405,R09365 RC00035,RC00113,RC01241
## 2 - - -
## BRITE KEGG_TC CAZy BiGG_Reaction
## 1 ko00000,ko00001,ko00002,ko01000 - - -
## 2 ko00000,ko00001,ko01000,ko04147 - - -
## PFAMs
## 1 Meth_synt_2
## 2 1-cysPrx_C,AhpC-TSAgogene <- merge(transcripts, eggnog, by=c("gene_id"), all=T)
gogene <- merge(gogene, kegg, by=c("gene_id"), all=T)
head(gogene,2)## gene_id seqnames
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t Pocillopora_acuta_HIv2___Sc0000013
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t Pocillopora_acuta_HIv2___Sc0000013
## start end width strand source type score.x phase
## 1 4542087 4551503 9417 + AUGUSTUS transcript NA NA
## 2 4639103 4647350 8248 + AUGUSTUS transcript NA NA
## ID
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t
## transcript_id seed_ortholog evalue score.y
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t 45351.EDO27354 2.41e-93 317
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t 6087.XP_002166004.2 1.28e-38 164
## eggNOG_OGs
## 1 COG0666@1|root,KOG0510@2759|Eukaryota,38G7Q@33154|Opisthokonta,3BCDU@33208|Metazoa
## 2 COG0666@1|root,KOG4177@2759|Eukaryota
## max_annot_lvl COG_category Description
## 1 33208|Metazoa DZ osmolarity-sensing cation channel activity
## 2 2759|Eukaryota I spectrin binding
## Preferred_name
## 1 TRPA1
## 2 -
## GOs
## 1 GO:0000302,GO:0001580,GO:0002791,GO:0002793,GO:0003008,GO:0003012,GO:0003674,GO:0004888,GO:0005034,GO:0005215,GO:0005216,GO:0005217,GO:0005244,GO:0005245,GO:0005261,GO:0005262,GO:0005488,GO:0005515,GO:0005575,GO:0005623,GO:0005886,GO:0005887,GO:0006810,GO:0006811,GO:0006812,GO:0006816,GO:0006873,GO:0006874,GO:0006875,GO:0006936,GO:0006939,GO:0006950,GO:0006979,GO:0007154,GO:0007165,GO:0007166,GO:0007204,GO:0007600,GO:0007602,GO:0007606,GO:0007610,GO:0007638,GO:0008150,GO:0008324,GO:0009266,GO:0009314,GO:0009408,GO:0009409,GO:0009410,GO:0009416,GO:0009453,GO:0009581,GO:0009582,GO:0009583,GO:0009593,GO:0009605,GO:0009612,GO:0009628,GO:0009636,GO:0009719,GO:0009966,GO:0009967,GO:0009987,GO:0010033,GO:0010035,GO:0010037,GO:0010243,GO:0010378,GO:0010646,GO:0010647,GO:0010817,GO:0014070,GO:0014074,GO:0014832,GO:0014848,GO:0015075,GO:0015085,GO:0015267,GO:0015276,GO:0015278,GO:0015318,GO:0016020,GO:0016021,GO:0016043,GO:0016048,GO:0016324,GO:0019233,GO:0019722,GO:0019725,GO:0019932,GO:0022607,GO:0022803,GO:0022832,GO:0022834,GO:0022836,GO:0022838,GO:0022839,GO:0022843,GO:0022857,GO:0022890,GO:0023041,GO:0023051,GO:0023052,GO:0023056,GO:0030001,GO:0030003,GO:0030424,GO:0031000,GO:0031224,GO:0031226,GO:0031644,GO:0031646,GO:0032024,GO:0032421,GO:0032501,GO:0032879,GO:0032880,GO:0032991,GO:0033554,GO:0033555,GO:0034220,GO:0034605,GO:0034702,GO:0034703,GO:0035556,GO:0035690,GO:0035774,GO:0036270,GO:0038023,GO:0040011,GO:0040040,GO:0042221,GO:0042330,GO:0042331,GO:0042391,GO:0042493,GO:0042542,GO:0042592,GO:0042752,GO:0042802,GO:0042995,GO:0043005,GO:0043052,GO:0043269,GO:0043270,GO:0043279,GO:0043933,GO:0044057,GO:0044070,GO:0044085,GO:0044425,GO:0044459,GO:0044464,GO:0045177,GO:0046677,GO:0046873,GO:0046883,GO:0046887,GO:0046957,GO:0048265,GO:0048518,GO:0048519,GO:0048522,GO:0048523,GO:0048583,GO:0048584,GO:0048878,GO:0050708,GO:0050714,GO:0050789,GO:0050794,GO:0050796,GO:0050801,GO:0050848,GO:0050850,GO:0050877,GO:0050896,GO:0050906,GO:0050907,GO:0050909,GO:0050912,GO:0050913,GO:0050951,GO:0050954,GO:0050955,GO:0050960,GO:0050961,GO:0050965,GO:0050966,GO:0050968,GO:0050974,GO:0050982,GO:0051046,GO:0051047,GO:0051049,GO:0051050,GO:0051179,GO:0051209,GO:0051222,GO:0051223,GO:0051234,GO:0051239,GO:0051240,GO:0051259,GO:0051260,GO:0051262,GO:0051282,GO:0051283,GO:0051289,GO:0051480,GO:0051606,GO:0051641,GO:0051649,GO:0051716,GO:0051930,GO:0051931,GO:0051969,GO:0052129,GO:0055065,GO:0055074,GO:0055080,GO:0055082,GO:0055085,GO:0060089,GO:0060341,GO:0060401,GO:0060402,GO:0061178,GO:0065003,GO:0065007,GO:0065008,GO:0070201,GO:0070417,GO:0070588,GO:0070838,GO:0070887,GO:0071241,GO:0071244,GO:0071310,GO:0071312,GO:0071313,GO:0071407,GO:0071415,GO:0071417,GO:0071466,GO:0071495,GO:0071840,GO:0071944,GO:0072347,GO:0072503,GO:0072507,GO:0072511,GO:0090087,GO:0090276,GO:0090277,GO:0097458,GO:0097553,GO:0097603,GO:0097604,GO:0098590,GO:0098655,GO:0098660,GO:0098662,GO:0098771,GO:0098796,GO:0098862,GO:0098900,GO:0098908,GO:0099094,GO:0099604,GO:0120025,GO:1901698,GO:1901699,GO:1901700,GO:1901701,GO:1902495,GO:1902531,GO:1902533,GO:1903522,GO:1903530,GO:1903532,GO:1903793,GO:1904058,GO:1904951,GO:1990351,GO:1990760
## 2 -
## EC KEGG_ko KEGG_Pathway KEGG_Module KEGG_Reaction KEGG_rclass
## 1 - ko:K04984 ko04750,map04750 - - -
## 2 - - - - - -
## BRITE KEGG_TC CAZy
## 1 ko00000,ko00001,ko04040 1.A.4.6.1,1.A.4.6.2,1.A.4.6.3,1.A.4.6.5 -
## 2 - - -
## BiGG_Reaction PFAMs KEGG_new
## 1 - Ank,Ank_2,Ank_3,Ank_4,Ion_trans K04984
## 2 - Ank,Ank_2,Ank_4,Ank_5,Ion_trans K04984dim(gogene)## [1] 33730 33geneInfo <- read.csv("../../output/WGCNA/WGCNA_ModuleMembership.csv") #this file was generated from the WGCNA analyses and contains the modules of interest
geneInfo<- plyr::rename(geneInfo, c("X"="gene_id"))
dim(geneInfo) # there are 9012 genes in our gene info file## [1] 9012 40geneInfo <- merge(gogene, geneInfo, by=c("gene_id")) #merging the GO and Kegg info to module membership for the 9012 genesFormat GO terms to remove dashes and quotes and separate by semicolons (replace , with ;) in GOs column
geneInfo$GOs <- gsub(",", ";", geneInfo$GOs)
geneInfo$GOs <- gsub('"', "", geneInfo$GOs)
geneInfo$GOs <- gsub("-", NA, geneInfo$GOs)
geneInfo$KEGG_new[geneInfo$KEGG_new == ""] <- NAunique(geneInfo$moduleColor)## [1] "green" "blue" "salmon" "turquoise" "yellow"
## [6] "black" "red" "magenta" "lightcyan" "purple"
## [11] "brown" "pink" "midnightblue" "tan" "cyan"geneInfo$Length<-lengths$transcript_lengths[match(geneInfo$gene_id, lengths$seqnames)]
dim(geneInfo)## [1] 9012 73write.csv(geneInfo, file = "../../output/WGCNA/GO_analysis/geneInfo_WGCNA.csv") #gene info for reference/supplementRun the GOSeq function by module color to test for GO term
enrichment. Due to high number of enriched GO terms, I am using an
adjusted p-value threshold of <0.001 and using rrvgo
package to reduce redundancy in list of GO terms.
Calcification - Up
### Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)### Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)calc_up_mods <- c("brown", "red", "black", "pink", "salmon", "blue")
nrow(geneInfo %>% dplyr::filter(moduleColor=="brown")) #942## [1] 942nrow(geneInfo %>% dplyr::filter(moduleColor=="red")) #425## [1] 425nrow(geneInfo %>% filter(moduleColor=="black")) #396## [1] 396nrow(geneInfo %>% filter(moduleColor=="pink")) #220## [1] 220nrow(geneInfo %>% filter(moduleColor=="salmon")) #154## [1] 154nrow(geneInfo %>% filter(moduleColor=="blue")) #1989## [1] 1989sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="brown")), nrow(geneInfo %>% dplyr::filter(moduleColor=="red")), nrow(geneInfo %>% filter(moduleColor=="black")), nrow(geneInfo %>% filter(moduleColor=="pink")), nrow(geneInfo %>% filter(moduleColor=="salmon")), nrow(geneInfo %>% filter(moduleColor=="blue")))## [1] 4126# 4126
calc_down_mods <- c("turquoise","magenta","lightcyan")
nrow(geneInfo %>% dplyr::filter(moduleColor=="turquoise")) #2558## [1] 2558nrow(geneInfo %>% dplyr::filter(moduleColor=="magenta")) #219## [1] 219nrow(geneInfo %>% filter(moduleColor=="lightcyan")) #65## [1] 65sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="turquoise")), nrow(geneInfo %>% dplyr::filter(moduleColor=="magenta")), nrow(geneInfo %>% filter(moduleColor=="lightcyan")))## [1] 2842# 2842
other_mods <- c("green","yellow", "purple", "midnightblue","cyan","tan")
sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="green")), nrow(geneInfo %>% dplyr::filter(moduleColor=="yellow")), nrow(geneInfo %>% filter(moduleColor=="purple")), nrow(geneInfo %>% filter(moduleColor=="midnightblue")), nrow(geneInfo %>% filter(moduleColor=="cyan")),nrow(geneInfo %>% filter(moduleColor=="tan")))## [1] 2044# 2044
# 4126 + 2842 + 2044 = 9012, which represents all of our genes4126 genes are in the 6 modules significantly upregulated by calcification.
### Generate vector with names in just the module we are analyzing
# ID.vector <- geneInfo %>%
# filter(moduleColor==c("brown", "red", "black", "pink", "salmon", "green")) %>%
# #get_rows(.data[[module]]))%>%
# pull(gene_id)
ID.vector <- geneInfo %>%
filter(moduleColor %in% c("brown", "red", "black", "pink", "salmon", "blue")) %>%
pull(gene_id)
length(ID.vector) #4126## [1] 4126##Get a list of GO Terms for each module
GO.terms <- geneInfo %>%
filter(moduleColor %in% c("brown", "red", "black", "pink", "salmon", "blue")) %>%
#filter(get_rows(.data[[module]]))%>%
dplyr::select(GOs,gene_id) %>% rename(GOs = "GO.terms")
dim(GO.terms) #4126 2## [1] 4126 2##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms$gene, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("gene", "GO.terms")
GO.terms<-split2##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector)
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) ## Warning in pcls(G): initial point very close to some inequality constraints
#run goseq using Wallenius method for all categories of GO terms
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columnsGO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"#adjust p-values
GO$bh_adjust <- p.adjust(GO$over_represented_pvalue, method="BH") #add adjusted p-values#Filtering for p < 0.05
GO_05 <- GO %>%
dplyr::filter(bh_adjust<0.05) %>%
dplyr::arrange(., ontology, bh_adjust)#Filtering for p < 0.00001
GO_00001 <- GO %>%
dplyr::filter(bh_adjust<0.00001) %>%
dplyr::arrange(., ontology, bh_adjust)
#Write file of results
write.csv(GO_00001, file = "../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv")GO_00001 <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv")go_results_BP <- GO_00001 %>%
filter(ontology=="BP")%>%
filter(bh_adjust != "NA") %>%
#filter(numInCat>10)%>%
arrange(., bh_adjust)
dim(go_results_BP)## [1] 1976 9head(go_results_BP)## X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0006807 0 1 1215
## 2 2 GO:0007275 0 1 978
## 3 3 GO:0008150 0 1 2099
## 4 4 GO:0008152 0 1 1387
## 5 5 GO:0009987 0 1 1938
## 6 6 GO:0016043 0 1 1049
## numInCat term ontology bh_adjust
## 1 1215 nitrogen compound metabolic process BP 0
## 2 978 multicellular organism development BP 0
## 3 2099 biological_process BP 0
## 4 1387 metabolic process BP 0
## 5 1938 cellular process BP 0
## 6 1049 cellular component organization BP 0Collapsing with simplifyEnrichment package
library(simplifyEnrichment)
BP_terms <- go_results_BP$GOterm
Mat <- GO_similarity(BP_terms, ont = "BP", db = "org.Ce.eg.db")
#compare_clustering_methods(Mat, plot_type = "heatmap")
pdf(file = "../../output/WGCNA/GO_analysis/calc_up_mods_GOSEM.pdf", width = 7, height = 5)
simplifyGO(Mat, word_cloud_grob_param = list(max_width = 50), max_words=20)## Cluster 1976 terms by 'binary_cut'... 332 clusters, used 17.53028 secs.
## 'magick' package is suggested to install to give better rasterization.
##
## Set `ht_opt$message = FALSE` to turn off this message.
## Perform keywords enrichment for 7 GO lists...dev.off()## quartz_off_screen
## 2CalcUpMods_GO_P00001 <- go_results_BP %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalueover_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Upregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcUpMods_GO_P00001
ggsave("../../output/WGCNA/GO_analysis/CalcUpMods_GO_P00001.pdf", CalcUpMods_GO_P00001, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results_BP$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")## preparing gene to GO mapping data...## preparing IC data... #calculate similarity
scores <- setNames(-log(go_results_BP$bh_adjust), go_results_BP$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
dim(reducedTerms)## [1] 1682 10#keep only the goterms from the reduced list
go_results_BP_reduced <- go_results_BP %>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results_BP_reduced$ParentTerm <- reducedTerms$parentTerm[match(go_results_BP_reduced$GOterm, reducedTerms$go)]
length(unique(go_results_BP_reduced$ParentTerm))## [1] 130The reduced list of terms is 1682 terms that falls under 130 parent terms.
write.csv(go_results_BP_reduced, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")Plot reduced terms
#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot_calcification <- ggplot(go_results_BP_reduced, aes(x = ontology, y = term)) +
geom_point(aes(size=bh_adjust)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot_calcification
ggsave(filename="../../output/WGCNA/GO_analysis/GO.plot_calcification.png", plot=GO.plot_calcification, dpi=300, height=100, units="in", limitsize=FALSE)## Saving 7 x 100 in imageCalcUpMods_GO_P00001_reduced <- go_results_BP_reduced %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalueover_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Upregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcUpMods_GO_P00001_reduced
ggsave("../../output/WGCNA/GO_analysis/CalcUpMods_GO_P00001_reduced.pdf", CalcUpMods_GO_P00001_reduced, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)Calcification - Down
2842 genes are in the 4 modules significantly downregulated by calcification.
### Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)### Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)### Generate vector with names in just the module we are analyzing
ID.vector <- geneInfo%>%
filter(moduleColor %in% c("turquoise","magenta","lightcyan"))%>%
pull(gene_id)
length(ID.vector) #2842## [1] 2842##Get a list of GO Terms for each module
GO.terms <- geneInfo%>%
filter(moduleColor %in% c("turquoise","magenta","lightcyan"))%>%
dplyr::select(GOs,gene_id) %>% rename(GOs = "GO.terms")
dim(GO.terms) #2842 2## [1] 2842 2##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms$gene, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("gene", "GO.terms")
GO.terms<-split2##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector)
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) 
#run goseq using Wallenius method for all categories of GO terms
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columnsGO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"#adjust p-values
GO$bh_adjust <- p.adjust(GO$over_represented_pvalue, method="BH") #add adjusted p-values#Filtering for p < 0.05
GO_05 <- GO %>%
dplyr::filter(bh_adjust<0.05) %>%
dplyr::arrange(., ontology, bh_adjust)#Filtering for p < 0.00001
GO_00001 <- GO %>%
dplyr::filter(bh_adjust<0.00001) %>%
dplyr::arrange(., ontology, bh_adjust)
#Write file of results
write.csv(GO_00001, file = "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv")GO_00001 <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv")go_results_BP <- GO_00001 %>%
filter(ontology=="BP") %>%
filter(bh_adjust != "NA") %>%
#filter(numInCat > 10) %>%
arrange(., bh_adjust)
dim(go_results_BP)## [1] 1824 9head(go_results_BP)## X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0006807 0 1 910
## 2 2 GO:0007275 0 1 610
## 3 3 GO:0008150 0 1 1386
## 4 4 GO:0008152 0 1 998
## 5 5 GO:0009987 0 1 1277
## 6 6 GO:0016043 0 1 652
## numInCat term ontology bh_adjust
## 1 910 nitrogen compound metabolic process BP 0
## 2 610 multicellular organism development BP 0
## 3 1386 biological_process BP 0
## 4 998 metabolic process BP 0
## 5 1277 cellular process BP 0
## 6 652 cellular component organization BP 0Collapsing with simplifyEnrichment package
library(simplifyEnrichment)
BP_terms <- go_results_BP$GOterm
Mat <- GO_similarity(BP_terms, ont = "BP", db = "org.Ce.eg.db")
pdf(file = "../../output/WGCNA/GO_analysis/calc_down_mods_GOSEM.pdf", width = 7, height = 5)
simplifyGO(Mat, word_cloud_grob_param = list(max_width = 50), max_words=20)## Cluster 1824 terms by 'binary_cut'... 287 clusters, used 14.12036 secs.
## 'magick' package is suggested to install to give better rasterization.
##
## Set `ht_opt$message = FALSE` to turn off this message.
## Perform keywords enrichment for 6 GO lists...dev.off()## quartz_off_screen
## 2CalcDownMods_GO_P00001 <- go_results_BP %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalueover_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Downregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcDownMods_GO_P00001
ggsave("../../output/WGCNA/GO_analysis/CalcDownMods_GO_P00001.pdf", CalcDownMods_GO_P00001, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results_BP$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")## preparing gene to GO mapping data...## preparing IC data... #calculate similarity
scores <- setNames(-log(go_results_BP$bh_adjust), go_results_BP$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
dim(reducedTerms)## [1] 1591 10#keep only the goterms from the reduced list
go_results_BP_reduced <- go_results_BP %>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results_BP_reduced$ParentTerm <- reducedTerms$parentTerm[match(go_results_BP_reduced$GOterm, reducedTerms$go)]
length(unique(go_results_BP_reduced$ParentTerm))## [1] 127The reduced list of terms is 1591 terms that falls under 127 parent terms.
write.csv(go_results_BP_reduced, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot_calcification_down <- ggplot(go_results_BP_reduced, aes(x = ontology, y = term)) +
geom_point(aes(size=bh_adjust)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot_calcification_down
ggsave(filename="../../output/WGCNA/GO_analysis/GO.plot_calcification_down.png", plot=GO.plot_calcification_down, dpi=300, height=100, units="in", limitsize=FALSE)## Saving 7 x 100 in imageCalcDownMods_GO_P00001_reduced <- go_results_BP_reduced %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalueover_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Downregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcDownMods_GO_P00001_reduced
ggsave("../../output/WGCNA/GO_analysis/CalcDownMods_GO_P00001_reduced.pdf", CalcDownMods_GO_P00001_reduced, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)code for by module
library(rrvgo)
# Define the unique module colors
module_colors <- na.omit(unique(geneInfo$moduleColor))
# Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)
# Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)
# Loop over each unique module color
for (color in module_colors) {
# Filter geneInfo based on the current color
color_filtered <- geneInfo %>% filter(moduleColor == color)
# Generate vector with names in just the module we are analyzing
ID.vector <- color_filtered$gene_id
length(ID.vector)
# Get a list of GO Terms for each module
GO.terms <- color_filtered %>%
dplyr::select(GOs, gene_id) %>%
rename(GOs = "GO.terms")
dim(GO.terms)
## Format to have one GO term per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms$gene_id, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("gene", "GO.terms")
GO.terms <- split2
## Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector <- as.integer(ALL.vector %in% ID.vector)
names(gene.vector) <- ALL.vector # set names
# Weight gene vector by bias for length of gene
pwf <- nullp(gene.vector, ID.vector, bias.data = LENGTH.vector)
# Run goseq using Wallenius method for all categories of GO terms
GO.wall <- goseq(pwf, ID.vector, gene2cat = GO.terms, test.cats = c("GO:BP", "GO:MF", "GO:CC"), method = "Wallenius", use_genes_without_cat = TRUE)
GO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"
# Adjust p-values
GO$bh_adjust <- p.adjust(GO$over_represented_pvalue, method = "BH")
# Filtering for p < 0.01
GO <- GO %>%
dplyr::filter(bh_adjust < 0.00001) %>%
dplyr::arrange(., ontology, bh_adjust)
# Write file of results
write.csv(GO, file = paste0("../../output/WGCNA/GO_analysis/goseq_pattern_", color, ".csv"))
go_results <- GO
go_results<-go_results%>%
filter(ontology=="BP")%>%
filter(bh_adjust != "NA") %>%
#filter(numInCat>100)%>%
arrange(., bh_adjust)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")
#calculate similarity
scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
#keep only the goterms from the reduced list
go_results <- go_results %>% filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]
#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot <- ggplot(go_results, aes(x = ontology, y = term)) +
geom_point(aes(size=bh_adjust)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot
length(colnames(go_results)[go_results$ParentTerm=="cation transport"])
length(colnames(go_results)[go_results$ParentTerm=="inorganic ion homeostasis"])
length(colnames(go_results)[go_results$ParentTerm=="regulation of cellular response to stress"])
}Specific differentially expressed genes
wgcna_counts_filtered<-read.csv("../../output/Filtered_gene_count_matrix.csv", strip.white=T)
wgcna_counts_filtered<- plyr::rename(wgcna_counts_filtered, c("X"="Gene"))colnames(wgcna_counts_filtered)## [1] "Gene" "RF13B" "RF13D" "RF14B" "RF14C" "RF15B" "RF15D" "RF17B" "RF17D"
## [10] "RF18B" "RF18D" "RF19B" "RF19C" "RF20B" "RF20C" "RF22B" "RF22C" "RF23A"
## [19] "RF23C" "RF24B" "RF24D" "RF25A" "RF25C" "RS11B" "RS11D" "RS12A" "RS12C"
## [28] "RS13A" "RS13C" "RS14B" "RS14C" "RS15B" "RS15D" "RS1B" "RS1C" "RS2B"
## [37] "RS2C" "RS3B" "RS3D" "RS6A" "RS6D" "RS7B" "RS7C" "RS8B" "RS8C"
## [46] "RS9A" "RS9C"library(tidyr)
wgcna_counts_filtered_long <- pivot_longer(wgcna_counts_filtered, cols=2:47, names_to = "Colony", values_to = "Counts")
wgcna_counts_filtered_long$Colony <- as.factor(wgcna_counts_filtered_long$Colony)
head(wgcna_counts_filtered_long)## # A tibble: 6 × 3
## Gene Colony Counts
## <chr> <fct> <int>
## 1 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF13B 61
## 2 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF13D 73
## 3 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF14B 62
## 4 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF14C 51
## 5 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF15B 31
## 6 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF15D 37wgcna_counts_filtered_long <- wgcna_counts_filtered_long %>%
separate(Colony, into = c('Origin', 'Colony.number'), sep = 2)
head(wgcna_counts_filtered_long)## # A tibble: 6 × 4
## Gene Origin Colony.number Counts
## <chr> <chr> <chr> <int>
## 1 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF 13B 61
## 2 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF 13D 73
## 3 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF 14B 62
## 4 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF 14C 51
## 5 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF 15B 31
## 6 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF 15D 37library(stringr)
wgcna_counts_filtered_long$Colony <- as.numeric(str_extract(wgcna_counts_filtered_long$Colony.number, "[0-9]+"))
wgcna_counts_filtered_long<-wgcna_counts_filtered_long %>%
mutate(Treatment = trimws(str_remove(wgcna_counts_filtered_long$Colony.number, "(\\s+[A-Za-z]+)?[0-9-]+")))
head(wgcna_counts_filtered_long)## # A tibble: 6 × 6
## Gene Origin Colony.number Counts Colony Treatment
## <chr> <chr> <chr> <int> <dbl> <chr>
## 1 Pocillopora_acuta_HIv2___RNAseq.… RF 13B 61 13 B
## 2 Pocillopora_acuta_HIv2___RNAseq.… RF 13D 73 13 D
## 3 Pocillopora_acuta_HIv2___RNAseq.… RF 14B 62 14 B
## 4 Pocillopora_acuta_HIv2___RNAseq.… RF 14C 51 14 C
## 5 Pocillopora_acuta_HIv2___RNAseq.… RF 15B 31 15 B
## 6 Pocillopora_acuta_HIv2___RNAseq.… RF 15D 37 15 Dwgcna_counts_filtered_long$Origin <- as.factor(wgcna_counts_filtered_long$Origin)
wgcna_counts_filtered_long$Treatment <- as.factor(wgcna_counts_filtered_long$Treatment)wgcna_counts_filtered_long <- wgcna_counts_filtered_long %>%
mutate(Treatment2 = ifelse(Treatment == "A" | Treatment == "B", "Variable",
ifelse(Treatment == "C" | Treatment == "D", "Stable", NA)))
wgcna_counts_filtered_long$Treatment2 <- as.factor(wgcna_counts_filtered_long$Treatment2)SLC4A7
wgcna_counts_filtered_long_SLC4A7<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g7402.t1")library(nlme)##
## Attaching package: 'nlme'## The following object is masked from 'package:IRanges':
##
## collapse## The following object is masked from 'package:dplyr':
##
## collapselibrary(emmeans)
SLC4A7.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_SLC4A7, na.action=na.exclude)
car::Anova(SLC4A7.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 11.9704 1 0.0005405 ***
## Origin 0.4869 1 0.4853006
## Treatment 1.0445 3 0.7904802
## Origin:Treatment 1.3516 3 0.7169263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(SLC4A7.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 654 61.5 19 525 783
## RS 453 54.2 19 339 566
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 201 80.8 19 2.491 0.0221
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
SLC4A7_sum<-summarySE(wgcna_counts_filtered_long_SLC4A7, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
SLC4A7_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 710.3636 265.8632 80.16078 178.6093
## 2 RF Variable 11 609.8182 241.3930 72.78272 162.1700
## 3 RS Stable 12 463.8333 248.6454 71.77773 157.9817
## 4 RS Variable 12 469.8333 166.4407 48.04730 105.7514Figure
pd<- position_dodge(0.2)
SLC4A7_fig<-ggplot(data=SLC4A7_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_SLC4A7,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(SLC4A7~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statSLC4A7_fig
### SLC4A3
wgcna_counts_filtered_long_SLC4A3 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g27873.t1")
wgcna_counts_filtered_long_SLC4A3 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 52 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 65 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 105 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 52 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 22 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 50 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 44 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 94 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 58 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 60 18 D Stable
## # ℹ 36 more rowsSLC4A3.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_SLC4A3 , na.action=na.exclude)
car::Anova(SLC4A3.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 24.6847 1 6.752e-07 ***
## Origin 1.7488 1 0.1860
## Treatment 0.4857 3 0.9220
## Origin:Treatment 1.1274 3 0.7705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(SLC4A3.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 73.1 5.98 19 60.6 85.6
## RS 58.5 5.28 19 47.4 69.6
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 14.6 7.81 19 1.866 0.0776
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
SLC4A3_sum<-summarySE(wgcna_counts_filtered_long_SLC4A3 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
SLC4A3_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 70.63636 21.65767 6.530032 14.54982
## 2 RF Variable 11 71.72727 27.65896 8.339491 18.58154
## 3 RS Stable 12 61.50000 22.71763 6.558016 14.43410
## 4 RS Variable 12 56.33333 17.55166 5.066726 11.15179Figure
pd<- position_dodge(0.2)
SLC4A3_fig<-ggplot(data=SLC4A3_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_SLC4A3 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(SLC4A3 ~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statSLC4A3_fig
NHE3
wgcna_counts_filtered_long_NHE3<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g24868.t1")
wgcna_counts_filtered_long_NHE3## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 88 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 93 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 143 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 96 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 123 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 136 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 87 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 111 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 95 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 87 18 D Stable
## # ℹ 36 more rowsNHE3.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_NHE3, na.action=na.exclude)
car::Anova(NHE3.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 45.5717 1 1.472e-11 ***
## Origin 1.1334 1 0.2870
## Treatment 5.3488 3 0.1480
## Origin:Treatment 5.9678 3 0.1132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(NHE3.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 111 6.90 19 96.9 126
## RS 102 6.19 19 89.0 115
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 9.42 8.58 19 1.098 0.2861
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
NHE3_sum<-summarySE(wgcna_counts_filtered_long_NHE3, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
NHE3_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 105.18182 15.16455 4.572284 10.18768
## 2 RF Variable 11 121.63636 33.62818 10.139278 22.59172
## 3 RS Stable 12 106.66667 22.81281 6.585491 14.49457
## 4 RS Variable 12 97.83333 22.00757 6.353040 13.98295Figure
pd<- position_dodge(0.2)
NHE3_fig<-ggplot(data=NHE3_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_NHE3,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(NHE3~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statNHE3_fig
CA1
wgcna_counts_filtered_long_CA1<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g12304.t1")
wgcna_counts_filtered_long_CA1## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 2854 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 4635 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 2949 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 4681 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 7704 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 8665 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 3948 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 3887 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 6896 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 6597 18 D Stable
## # ℹ 36 more rowsCA1.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_CA1, na.action=na.exclude)
car::Anova(CA1.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 7.3722 1 0.006624 **
## Origin 0.7737 1 0.379079
## Treatment 2.6212 3 0.453784
## Origin:Treatment 2.3227 3 0.508194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(CA1.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 5178 542 19 4045 6312
## RS 2568 479 19 1565 3571
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 2611 704 19 3.709 0.0015
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
CA1_sum<-summarySE(wgcna_counts_filtered_long_CA1, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA1_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 5970.636 2621.084 790.2864 1760.8679
## 2 RF Variable 11 4733.455 1986.062 598.8204 1334.2549
## 3 RS Stable 12 2653.583 1847.177 533.2340 1173.6400
## 4 RS Variable 12 2775.250 1463.636 422.5154 929.9501Figure
pd<- position_dodge(0.2)
CA1_fig<-ggplot(data=CA1_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_CA1,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(CA1~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statCA1_fig
### CA2
wgcna_counts_filtered_long_CA2<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g13824.t1")
wgcna_counts_filtered_long_CA2## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 139 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 164 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 169 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 249 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 174 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 232 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 232 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 204 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 244 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 338 18 D Stable
## # ℹ 36 more rowsCA2.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_CA2, na.action=na.exclude)
car::Anova(CA2.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 12.1927 1 0.0004798 ***
## Origin 8.6459 1 0.0032780 **
## Treatment 2.6667 3 0.4459141
## Origin:Treatment 1.7689 3 0.6217181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(CA2.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 148.05 16.4 19 113.8 182.3
## RS -4.05 15.0 19 -35.5 27.4
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 152 18.2 19 8.346 <.0001
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
CA2_sum<-summarySE(wgcna_counts_filtered_long_CA2, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA2_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 150.181818 98.679094 29.752866 66.293518
## 2 RF Variable 11 131.272727 70.816793 21.352066 47.575369
## 3 RS Stable 12 10.583333 9.894519 2.856302 6.286678
## 4 RS Variable 12 9.916667 8.106769 2.340223 5.150795Figure
pd<- position_dodge(0.2)
CA2_fig<-ggplot(data=CA2_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_CA2,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(CA2~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statCA2_fig
### VHA
wgcna_counts_filtered_long_VHA<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g23064.t1")
wgcna_counts_filtered_long_VHA## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 28 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 19 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 26 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 21 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 35 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 30 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 30 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 21 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 16 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 17 18 D Stable
## # ℹ 36 more rowsVHA.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_VHA, na.action=na.exclude)
car::Anova(VHA.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 17.3800 1 3.06e-05 ***
## Origin 0.5371 1 0.4636
## Treatment 2.5674 3 0.4632
## Origin:Treatment 2.9805 3 0.3946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(VHA.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 24.6 2.29 19 19.8 29.3
## RS 24.9 2.01 19 20.7 29.1
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -0.353 3.02 19 -0.117 0.9080
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
VHA_sum<-summarySE(wgcna_counts_filtered_long_VHA, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
VHA_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 21.54545 5.317210 1.603199 3.572151
## 2 RF Variable 11 27.36364 5.427204 1.636364 3.646045
## 3 RS Stable 12 26.75000 13.784873 3.979350 8.758491
## 4 RS Variable 12 23.41667 7.025387 2.028054 4.463718Figure
pd<- position_dodge(0.2)
VHA_fig<-ggplot(data=VHA_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_VHA,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(VHA~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statVHA_fig
### HSP90
wgcna_counts_filtered_long_HSP90<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g6656.t1")
wgcna_counts_filtered_long_HSP90## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 908 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 778 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 1301 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 891 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 1043 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 948 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 1547 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 906 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 1425 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 1568 18 D Stable
## # ℹ 36 more rowsHSP90.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_HSP90, na.action=na.exclude)
car::Anova(HSP90.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 34.6376 1 3.972e-09 ***
## Origin 0.5404 1 0.4623
## Treatment 2.0956 3 0.5528
## Origin:Treatment 0.7946 3 0.8508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(HSP90.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 1345 92.2 19 1152 1538
## RS 1102 80.8 19 933 1271
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 243 123 19 1.985 0.0618
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
HSP90_sum<-summarySE(wgcna_counts_filtered_long_HSP90, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
HSP90_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 1218.1818 382.7918 115.41607 257.1630
## 2 RF Variable 11 1426.5455 363.5721 109.62111 244.2511
## 3 RS Stable 12 969.3333 311.6707 89.97157 198.0261
## 4 RS Variable 12 1274.5833 397.4287 114.72777 252.5141Figure
pd<- position_dodge(0.2)
HSP90_fig<-ggplot(data=HSP90_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_HSP90,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(HSP90~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statHSP90_fig
HIF1A
wgcna_counts_filtered_long_HIF1A <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g3039.t1")
wgcna_counts_filtered_long_HIF1A ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 172 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 158 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 99 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 159 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 127 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 164 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 93 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 128 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 104 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 213 18 D Stable
## # ℹ 36 more rowsHIF1A.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_HIF1A , na.action=na.exclude)
car::Anova(HIF1A.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 23.0105 1 1.611e-06 ***
## Origin 0.0263 1 0.8712
## Treatment 2.1843 3 0.5350
## Origin:Treatment 2.0693 3 0.5582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(HIF1A.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 146 10.55 19 124 169
## RS 174 9.41 19 155 194
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -28 13.4 19 -2.097 0.0496
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
HIF1A_sum<-summarySE(wgcna_counts_filtered_long_HIF1A , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
HIF1A_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 151.5455 28.24310 8.515615 18.97397
## 2 RF Variable 11 141.2727 33.79672 10.190094 22.70494
## 3 RS Stable 12 192.0833 51.70627 14.926313 32.85259
## 4 RS Variable 12 165.1667 34.39565 9.929168 21.85395Figure
pd<- position_dodge(0.2)
HIF1A_fig<-ggplot(data=HIF1A_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_HIF1A ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(HIF1A ~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statHIF1A_fig
HSP70
wgcna_counts_filtered_long_HSP70 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g10659.t1")
wgcna_counts_filtered_long_HSP70 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 57 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 57 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 71 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 36 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 74 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 78 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 92 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 59 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 54 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 36 18 D Stable
## # ℹ 36 more rowsHSP70.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_HSP70 , na.action=na.exclude)
car::Anova(HSP70.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 10.4968 1 0.001196 **
## Origin 0.0701 1 0.791247
## Treatment 1.9098 3 0.591333
## Origin:Treatment 8.0987 3 0.044015 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(HSP70.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 63.1 10.78 19 40.5 85.6
## RS 103.0 9.72 19 82.7 123.3
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -40 13.1 19 -3.043 0.0067
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
HSP70_sum<-summarySE(wgcna_counts_filtered_long_HSP70 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
HSP70_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 59.09091 15.70003 4.733737 10.54742
## 2 RF Variable 11 71.63636 20.51474 6.185427 13.78199
## 3 RS Stable 12 116.00000 64.30750 18.563976 40.85904
## 4 RS Variable 12 95.33333 31.31463 9.039755 19.89637Figure
pd<- position_dodge(0.2)
HSP70_fig<-ggplot(data=HSP70_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_HSP70 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(HSP70 ~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statHSP70_fig
PRKCD
wgcna_counts_filtered_long_PRKCD <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g25259.t1")
wgcna_counts_filtered_long_PRKCD ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 7 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 9 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 47 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 18 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 25 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 62 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 22 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 35 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 28 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 23 18 D Stable
## # ℹ 36 more rowsPRKCD.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_PRKCD , na.action=na.exclude)
car::Anova(PRKCD.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 9.0491 1 0.002628 **
## Origin 3.7499 1 0.052810 .
## Treatment 5.4359 3 0.142525
## Origin:Treatment 2.4700 3 0.480731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(PRKCD.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 25.40 3.25 19 18.60 32.2
## RS 7.18 2.91 19 1.09 13.3
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 18.2 4.04 19 4.508 0.0002
##
## Results are averaged over the levels of: Treatment
## Degrees-of-freedom method: containmentlibrary(Rmisc)
PRKCD_sum<-summarySE(wgcna_counts_filtered_long_PRKCD , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
PRKCD_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 25.000000 17.487138 5.272571 11.748019
## 2 RF Variable 11 26.636364 14.955084 4.509128 10.046962
## 3 RS Stable 12 7.333333 7.749878 2.237197 4.924037
## 4 RS Variable 12 7.083333 7.403419 2.137183 4.703908Figure
pd<- position_dodge(0.2)
PRKCD_fig<-ggplot(data=PRKCD_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(PRKCD ~expression))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statPRKCD_fig
compare_figs<-cowplot::plot_grid(SLC4A7_fig, NHE3_fig, CA1_fig, CA2_fig, HSP70_fig,HIF1A_fig, nrow=3)
compare_figs
Biomineralization toolkit present in modules
biomin <-read.csv("../../output/Biomin_blast_Pocillopora_acuta_best_hit.csv")wgcnamod <-read.csv("../../output/WGCNA/WGCNA_ModuleMembership.csv")
wgcnamod<- plyr::rename(wgcnamod, c("X"="Pocillopora_acuta_best_hit"))biomin_mod <- merge(biomin, wgcnamod, by=c("Pocillopora_acuta_best_hit"), all=F)
head(biomin_mod)## Pocillopora_acuta_best_hit accessionnumber.geneID
## 1 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 XP_022804785.1
## 2 Pocillopora_acuta_HIv2___RNAseq.g11609.t1 P33_g8985
## 3 Pocillopora_acuta_HIv2___RNAseq.g13172.t1 JR972076.1
## 4 Pocillopora_acuta_HIv2___RNAseq.g13172.t1 Gene:g13552
## 5 Pocillopora_acuta_HIv2___RNAseq.g13172.t1 aug_v2a.06327.t1
## 6 Pocillopora_acuta_HIv2___RNAseq.g13823.t1 PFX18785.1
## definition
## 1 thioredoxin reductase 1, cytoplasmic-like [Stylophora pistillata]
## 2 Flagellar associated protein
## 3 Acidic skeletal organic matrix protein (Acidic SOMP)
## 4 Acidic SOMP (Full-Length p27)
## 5 SAARP3
## 6 Mucin-4 [Stylophora pistillata]
## Ref substanceBXH
## 1 Peled et al., 2020 Pocillopora_acuta_HIv2___RNAseq.g10093.t2
## 2 Drake et al., 2013 Pocillopora_acuta_HIv2___RNAseq.g11609.t1
## 3 Ramos-Silva et al., 2013 Pocillopora_acuta_HIv2___RNAseq.g13172.t1
## 4 Mummadisetti et al., 2021 Pocillopora_acuta_HIv2___RNAseq.g13172.t1
## 5 Takeuchi et al., 2016 Pocillopora_acuta_HIv2___RNAseq.g13172.t1
## 6 Peled et al., 2020 Pocillopora_acuta_HIv2___RNAseq.g13823.t1
## geneSymbol moduleColor
## 1 Pocillopora_acuta_HIv2___Sc0000021 brown
## 2 Pocillopora_acuta_HIv2___Sc0000013 turquoise
## 3 Pocillopora_acuta_HIv2___Sc0000004 red
## 4 Pocillopora_acuta_HIv2___Sc0000004 red
## 5 Pocillopora_acuta_HIv2___Sc0000004 red
## 6 Pocillopora_acuta_HIv2___Sc0000005 pink
## GO.terms
## 1 GO:0000003,GO:0000302,GO:0000305,GO:0001650,GO:0001704,GO:0001707,GO:0001887,GO:0001890,GO:0003006,GO:0003674,GO:0003824,GO:0004791,GO:0005488,GO:0005515,GO:0005575,GO:0005622,GO:0005623,GO:0005634,GO:0005654,GO:0005730,GO:0005737,GO:0005739,GO:0005783,GO:0005829,GO:0006082,GO:0006139,GO:0006518,GO:0006520,GO:0006575,GO:0006725,GO:0006732,GO:0006733,GO:0006739,GO:0006749,GO:0006753,GO:0006790,GO:0006793,GO:0006796,GO:0006807,GO:0006950,GO:0006979,GO:0007154,GO:0007165,GO:0007275,GO:0007369,GO:0007498,GO:0008150,GO:0008152,GO:0008283,GO:0009056,GO:0009069,GO:0009117,GO:0009611,GO:0009628,GO:0009636,GO:0009653,GO:0009790,GO:0009888,GO:0009987,GO:0010035,GO:0010038,GO:0010269,GO:0010941,GO:0010942,GO:0012505,GO:0015036,GO:0015949,GO:0016043,GO:0016174,GO:0016209,GO:0016259,GO:0016491,GO:0016651,GO:0016667,GO:0016668,GO:0016999,GO:0017001,GO:0017144,GO:0018996,GO:0019216,GO:0019222,GO:0019362,GO:0019637,GO:0019725,GO:0019752,GO:0022404,GO:0022414,GO:0022607,GO:0023052,GO:0031974,GO:0031981,GO:0032501,GO:0032502,GO:0033554,GO:0033797,GO:0034599,GO:0034641,GO:0036295,GO:0036296,GO:0036477,GO:0042221,GO:0042303,GO:0042395,GO:0042493,GO:0042537,GO:0042592,GO:0042737,GO:0042743,GO:0042744,GO:0042802,GO:0042803,GO:0043025,GO:0043167,GO:0043169,GO:0043226,GO:0043227,GO:0043228,GO:0043229,GO:0043231,GO:0043232,GO:0043233,GO:0043436,GO:0043603,GO:0043933,GO:0044085,GO:0044237,GO:0044238,GO:0044248,GO:0044281,GO:0044297,GO:0044422,GO:0044424,GO:0044428,GO:0044444,GO:0044446,GO:0044452,GO:0044464,GO:0045340,GO:0045454,GO:0046483,GO:0046496,GO:0046688,GO:0046872,GO:0046914,GO:0046983,GO:0048332,GO:0048513,GO:0048518,GO:0048522,GO:0048598,GO:0048608,GO:0048646,GO:0048678,GO:0048729,GO:0048731,GO:0048856,GO:0050664,GO:0050789,GO:0050794,GO:0050896,GO:0051186,GO:0051187,GO:0051259,GO:0051262,GO:0051716,GO:0055086,GO:0055093,GO:0055114,GO:0061458,GO:0065003,GO:0065007,GO:0065008,GO:0070013,GO:0070276,GO:0070482,GO:0070887,GO:0070995,GO:0071241,GO:0071248,GO:0071280,GO:0071453,GO:0071455,GO:0071704,GO:0071840,GO:0072524,GO:0072593,GO:0080090,GO:0097237,GO:0097458,GO:0098623,GO:0098625,GO:0098626,GO:0098754,GO:0098869,GO:1901360,GO:1901564,GO:1901605,GO:1901700,GO:1990748
## 2 -
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## GO.description GS.Flat GS.Slope p.GS.Flat
## 1 thioredoxin-disulfide reductase activity 0.57178848 -0.57178848 3.311055e-05
## 2 - -0.29586493 0.29586493 4.589336e-02
## 3 <NA> 0.35628512 -0.35628512 1.508700e-02
## 4 <NA> 0.35628512 -0.35628512 1.508700e-02
## 5 <NA> 0.35628512 -0.35628512 1.508700e-02
## 6 <NA> -0.05455251 0.05455251 7.187880e-01
## p.GS.Slope A.brown p.A.brown A.magenta p.A.magenta A.red
## 1 3.311055e-05 0.7005073 5.973619e-08 -0.3738439 1.048844e-02 0.2901298
## 2 4.589336e-02 -0.4291375 2.921081e-03 0.3115539 3.505853e-02 -0.3452015
## 3 1.508700e-02 0.4914202 5.241968e-04 -0.6288605 2.864308e-06 0.6673892
## 4 1.508700e-02 0.4914202 5.241968e-04 -0.6288605 2.864308e-06 0.6673892
## 5 1.508700e-02 0.4914202 5.241968e-04 -0.6288605 2.864308e-06 0.6673892
## 6 7.187880e-01 0.0972208 5.203783e-01 -0.3252127 2.743096e-02 0.3709019
## p.A.red A.turquoise p.A.turquoise A.purple p.A.purple A.green
## 1 5.047695e-02 -0.43323233 2.634293e-03 0.6984202 6.792759e-08 0.4574538
## 2 1.879503e-02 0.58815287 1.720729e-05 -0.1784560 2.353887e-01 -0.1306835
## 3 4.071016e-07 -0.13892006 3.571825e-01 0.1198762 4.274677e-01 0.2378899
## 4 4.071016e-07 -0.13892006 3.571825e-01 0.1198762 4.274677e-01 0.2378899
## 5 4.071016e-07 -0.13892006 3.571825e-01 0.1198762 4.274677e-01 0.2378899
## 6 1.116224e-02 0.08164806 5.895928e-01 -0.1391597 3.563456e-01 0.1614616
## p.A.green A.lightcyan p.A.lightcyan A.pink p.A.pink A.blue
## 1 0.001391986 -0.3508191 1.682948e-02 0.1707384 2.565893e-01 0.12358439
## 2 0.386672688 0.1196505 4.283449e-01 -0.1522331 3.125037e-01 -0.58598406
## 3 0.111386103 -0.6473842 1.159989e-06 0.7188738 1.835918e-08 0.07448551
## 4 0.111386103 -0.6473842 1.159989e-06 0.7188738 1.835918e-08 0.07448551
## 5 0.111386103 -0.6473842 1.159989e-06 0.7188738 1.835918e-08 0.07448551
## 6 0.283719167 -0.5276145 1.646022e-04 0.6417477 1.537006e-06 -0.02286640
## p.A.blue A.salmon p.A.salmon A.midnightblue p.A.midnightblue
## 1 4.132051e-01 0.1178467 0.435389343 0.2439890 0.10224333
## 2 1.880492e-05 0.1907995 0.204028320 0.2258109 0.13131383
## 3 6.227429e-01 0.4254256 0.003204458 0.2691022 0.07053914
## 4 6.227429e-01 0.4254256 0.003204458 0.2691022 0.07053914
## 5 6.227429e-01 0.4254256 0.003204458 0.2691022 0.07053914
## 6 8.801027e-01 0.2940377 0.047315397 0.2906592 0.05003895
## A.black p.A.black A.cyan p.A.cyan A.yellow p.A.yellow
## 1 -0.28430645 0.05550307 0.04904562 0.7461773 0.05522073 0.7154873547
## 2 -0.18825739 0.21023361 0.07386502 0.6256510 -0.14392338 0.3399558326
## 3 0.09618758 0.52484209 0.16699226 0.2673276 -0.38010677 0.0091694889
## 4 0.09618758 0.52484209 0.16699226 0.2673276 -0.38010677 0.0091694889
## 5 0.09618758 0.52484209 0.16699226 0.2673276 -0.38010677 0.0091694889
## 6 0.02103556 0.88964060 -0.13338389 0.3768501 -0.46998526 0.0009821983
## A.tan p.A.tan
## 1 0.2648346 0.075293267
## 2 0.2613466 0.079363532
## 3 -0.2055446 0.170565420
## 4 -0.2055446 0.170565420
## 5 -0.2055446 0.170565420
## 6 -0.3805358 0.009084622plyr::count(biomin_mod, "moduleColor")## moduleColor freq
## 1 black 3
## 2 blue 36
## 3 brown 17
## 4 cyan 2
## 5 green 3
## 6 magenta 1
## 7 pink 7
## 8 red 18
## 9 salmon 6
## 10 tan 3
## 11 turquoise 21
## 12 yellow 10Format GO terms to remove dashes and quotes and separate by semicolons (replace , with ;) in GOs column
biomin_mod$GO.terms <- gsub(",", ";", biomin_mod$GO.terms)
biomin_mod$GO.terms <- gsub('"', "", biomin_mod$GO.terms)
biomin_mod$GO.terms <- gsub("-", NA, biomin_mod$GO.terms)GO terms
### Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)### Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)ID.vector_biomin <- biomin_mod %>%
#filter(moduleColor=="black")%>%
#get_rows(.data[[module]]))%>%
pull(Pocillopora_acuta_best_hit)
##Get a list of GO Terms for each module
GO.terms_biomin <- biomin_mod %>%
#filter(moduleColor=="black")%>%
#filter(get_rows(.data[[module]]))%>%
dplyr::select(GO.terms,Pocillopora_acuta_best_hit)##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms_biomin$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms_biomin$Pocillopora_acuta_best_hit, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("Pocillopora_acuta_best_hit", "GO.terms")
GO.terms_biomin<-split2
head(GO.terms_biomin)## Pocillopora_acuta_best_hit GO.terms
## 1 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0000003
## 2 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0000302
## 3 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0000305
## 4 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0001650
## 5 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0001704
## 6 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0001707#GO.terms_biomin_sub <- GO.terms_biomin%>%
#filter(Pocillopora_acuta_best_hit==c("Pocillopora_acuta_HIv2___RNAseq.g15280.t1","Pocillopora_acuta_HIv2___RNAseq.g7402.t1"))
#GO.terms_biomin_sub##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector)
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector)
#run goseq using Wallenius method for all categories of GO terms
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms_biomin, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columnsGO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"#adjust p-values
GO$bh_adjust <- p.adjust(GO$over_represented_pvalue, method="BH") #add adjusted p-values#Filtering for p < 0.01
GO <- GO %>%
#dplyr::filter(bh_adjust<0.05) %>%
dplyr::arrange(., ontology, bh_adjust)
#Write file of results
write.csv(GO, file = "../../output/WGCNA/GO_analysis/goseq_pattern_biomin.csv")#add vector for terms of interest to reduce number of GO terms - NOT using this to look at individual modules for exploratory purposes
keywords<-c("metabolism", "carbon","bicarbonate", "apoptosis", "death", "symbiosis", "regulation of cell communication", "trans membrane transport", "transmembrane", "organic substance transport", "inorganic substance transport","response to stress", "antioxidant", "calcification","biomineralization", "heat","transporters","proton transport","ion transport","acid-base", "homeostasis")go_results <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_biomin.csv") go_results<-go_results%>%
filter(ontology=="BP")%>%
filter(bh_adjust != "NA") %>%
#filter(numInCat>10)%>%
arrange(., bh_adjust)
head(go_results)## X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0010629 0.07725836 1 2
## 2 2 GO:0000278 0.09731254 1 2
## 3 3 GO:0007049 0.09731254 1 2
## 4 4 GO:0022402 0.09731254 1 2
## 5 5 GO:0030036 0.09731254 1 2
## 6 6 GO:1903047 0.09731254 1 2
## numInCat term ontology bh_adjust
## 1 2 negative regulation of gene expression BP 1
## 2 2 mitotic cell cycle BP 1
## 3 2 cell cycle BP 1
## 4 2 cell cycle process BP 1
## 5 2 actin cytoskeleton organization BP 1
## 6 2 mitotic cell cycle process BP 1library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")## preparing gene to GO mapping data...## preparing IC data...#calculate similarity
scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
head(reducedTerms)## go cluster parent score size
## GO:1901160 GO:1901160 91 GO:1901160 0 0
## GO:1901135 GO:1901135 90 GO:1901135 0 3
## GO:0098657 GO:0098657 89 GO:0098657 0 0
## GO:0042430 GO:0042430 88 GO:0042430 0 0
## GO:0042537 GO:0042537 78 GO:0042537 0 0
## GO:0006790 GO:0006790 77 GO:0006790 0 9
## term
## GO:1901160 primary amino compound metabolic process
## GO:1901135 carbohydrate derivative metabolic process
## GO:0098657 import into cell
## GO:0042430 indole-containing compound metabolic process
## GO:0042537 benzene-containing compound metabolic process
## GO:0006790 sulfur compound metabolic process
## parentTerm termUniqueness
## GO:1901160 primary amino compound metabolic process 0.9618047
## GO:1901135 carbohydrate derivative metabolic process 0.9590404
## GO:0098657 import into cell 0.9689427
## GO:0042430 indole-containing compound metabolic process 0.9439870
## GO:0042537 benzene-containing compound metabolic process 0.9569701
## GO:0006790 sulfur compound metabolic process 0.9534063
## termUniquenessWithinCluster termDispensability
## GO:1901160 1 0
## GO:1901135 1 0
## GO:0098657 1 0
## GO:0042430 1 0
## GO:0042537 1 0
## GO:0006790 1 0#keep only the goterms from the reduced list
go_results<-go_results%>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]
go_results<-go_results %>%
mutate(Factor = "Biomin")
head(go_results)## X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0010629 0.07725836 1 2
## 2 2 GO:0000278 0.09731254 1 2
## 3 3 GO:0007049 0.09731254 1 2
## 4 4 GO:0022402 0.09731254 1 2
## 5 5 GO:0030036 0.09731254 1 2
## 6 6 GO:1903047 0.09731254 1 2
## numInCat term ontology bh_adjust
## 1 2 negative regulation of gene expression BP 1
## 2 2 mitotic cell cycle BP 1
## 3 2 cell cycle BP 1
## 4 2 cell cycle process BP 1
## 5 2 actin cytoskeleton organization BP 1
## 6 2 mitotic cell cycle process BP 1
## ParentTerm Factor
## 1 negative regulation of biological process Biomin
## 2 regulation of cell cycle G1/S phase transition Biomin
## 3 regulation of cell cycle G1/S phase transition Biomin
## 4 regulation of cell cycle G1/S phase transition Biomin
## 5 actin filament severing Biomin
## 6 regulation of cell cycle G1/S phase transition Biominwrite.csv(go_results, "../../output/Biomineralization_goterms.csv")go_results<-go_results%>%
filter(grepl(paste(keywords, collapse="|"), ParentTerm))#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot_biomin <- ggplot(go_results, aes(x = ontology, y = term)) +
geom_point(aes(size=bh_adjust)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot_biomin
***thioredoxin reductase 1
wgcna_counts_filtered_long_g10093 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g10093.t2")
wgcna_counts_filtered_long_g10093 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 59 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 69 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 86 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 75 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 49 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 63 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 80 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 57 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 98 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 73 18 D Stable
## # ℹ 36 more rowsg10093.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g10093 , na.action=na.exclude)
car::Anova(g10093.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 215.5517 1 < 2.2e-16 ***
## Origin 7.8998 1 0.004944 **
## Treatment2 1.0425 1 0.307243
## Origin:Treatment2 2.7791 1 0.095503 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g10093.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 66.5 3.57 19 59.0 73.9
## RS 44.8 3.43 19 37.6 52.0
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 21.6 4.47 23 4.836 0.0001
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g10093_sum<-summarySE(wgcna_counts_filtered_long_g10093 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g10093_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 63.09091 12.70004 3.829205 8.532000
## 2 RF Variable 11 68.18182 19.91893 6.005782 13.381717
## 3 RS Stable 12 48.50000 12.57342 3.629634 7.988770
## 4 RS Variable 12 42.08333 12.44960 3.593889 7.910096Figure
pd<- position_dodge(0.2)
g10093_fig<-ggplot(data=g10093_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g10093,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Thioredoxin~reductase~1~expression))+
ggtitle(~blue)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg10093_fig
***Flagellar associated protein
wgcna_counts_filtered_long_g11609 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g11609.t1")
wgcna_counts_filtered_long_g11609 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 270 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 210 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 227 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 162 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 252 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 234 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 252 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 143 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 164 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 209 18 D Stable
## # ℹ 36 more rowsg11609.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g11609 , na.action=na.exclude)
car::Anova(g11609.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 55.8226 1 7.932e-14 ***
## Origin 10.3843 1 0.001271 **
## Treatment2 2.7386 1 0.097951 .
## Origin:Treatment2 9.2919 1 0.002302 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g11609.lme, list(pairwise ~ Treatment2:Origin), adjust = "tukey")
tukey3## $`emmeans of Treatment2, Origin`
## Treatment2 Origin emmean SE df lower.CL upper.CL
## Stable RF 213 28.5 19 153 273
## Variable RF 274 28.5 19 214 334
## Stable RS 337 27.3 19 280 395
## Variable RS 243 27.3 19 186 300
##
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Treatment2, Origin`
## 1 estimate SE df t.ratio p.value
## Stable RF - Variable RF -60.9 36.8 23 -1.655 0.3694
## Stable RF - Stable RS -124.1 38.5 23 -3.222 0.0184
## Stable RF - Variable RS -29.7 38.5 23 -0.772 0.8664
## Variable RF - Stable RS -63.2 38.5 23 -1.641 0.3763
## Variable RF - Variable RS 31.2 38.5 23 0.809 0.8494
## Stable RS - Variable RS 94.4 35.2 23 2.679 0.0601
##
## Degrees-of-freedom method: containment
## P value adjustment: tukey method for comparing a family of 4 estimateslibrary(Rmisc)
g11609_sum<-summarySE(wgcna_counts_filtered_long_g11609 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g11609_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 216.3636 68.28803 20.58961 45.87652
## 2 RF Variable 11 277.2727 116.48699 35.12215 78.25702
## 3 RS Stable 12 335.0833 113.94772 32.89387 72.39893
## 4 RS Variable 12 240.6667 69.27985 19.99937 44.01831Figure
pd<- position_dodge(0.2)
g11609_fig<-ggplot(data=g11609_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g11609 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Flagellar~associated~protein~expression))+
ggtitle(~turquoise)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg11609_fig
***Actin
wgcna_counts_filtered_long_g14505 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g14505.t1")
wgcna_counts_filtered_long_g14505 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 1418 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 1504 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 1744 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 1503 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 1805 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 1848 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 1405 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 1124 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 1361 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 1045 18 D Stable
## # ℹ 36 more rowsg14505.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g14505 , na.action=na.exclude)
car::Anova(g14505.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 134.3187 1 < 2.2e-16 ***
## Origin 7.2528 1 0.007079 **
## Treatment2 0.4459 1 0.504290
## Origin:Treatment2 2.6857 1 0.101254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g14505.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 1631 95.6 19 1431 1832
## RS 1919 91.6 19 1727 2110
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -287 132 23 -2.170 0.0406
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g14505_sum<-summarySE(wgcna_counts_filtered_long_g14505 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g14505_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 1567.545 325.2954 98.08024 218.5364
## 2 RF Variable 11 1695.273 508.7410 153.39119 341.7769
## 3 RS Stable 12 2071.833 491.8505 141.98500 312.5069
## 4 RS Variable 12 1765.583 441.5165 127.45483 280.5262Figure
pd<- position_dodge(0.2)
g14505_fig<-ggplot(data=g14505_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g14505 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Actin~expression))+
ggtitle(~turquoise)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg14505_fig
***CA2
wgcna_counts_filtered_long_CA2<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g13824.t1")
wgcna_counts_filtered_long_CA2## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 139 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 164 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 169 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 249 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 174 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 232 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 232 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 204 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 244 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 338 18 D Stable
## # ℹ 36 more rowsCA2.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_CA2, na.action=na.exclude)
car::Anova(CA2.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 96.0895 1 <2e-16 ***
## Origin 100.2939 1 <2e-16 ***
## Treatment2 2.1871 1 0.1392
## Origin:Treatment2 1.0620 1 0.3027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(CA2.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 155.76 15.6 19 123.1 188
## RS -8.88 15.2 19 -40.8 23
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 165 14.9 23 11.032 <.0001
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
CA2_sum<-summarySE(wgcna_counts_filtered_long_CA2, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA2_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 150.181818 98.679094 29.752866 66.293518
## 2 RF Variable 11 131.272727 70.816793 21.352066 47.575369
## 3 RS Stable 12 10.583333 9.894519 2.856302 6.286678
## 4 RS Variable 12 9.916667 8.106769 2.340223 5.150795Figure
pd<- position_dodge(0.2)
CA2_fig<-ggplot(data=CA2_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_CA2,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(CA2~expression))+
ggtitle(~blue)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statCA2_fig
Poly [ADP-ribose] polymerase 11
wgcna_counts_filtered_long_g14663 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g14663.t1a")
wgcna_counts_filtered_long_g14663 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 61 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 54 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 19 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 24 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 23 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 49 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 17 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 13 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 16 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 10 18 D Stable
## # ℹ 36 more rowsg14663.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g14663 , na.action=na.exclude)
car::Anova(g14663.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 14.3519 1 0.0001516 ***
## Origin 0.5329 1 0.4653839
## Treatment2 0.0388 1 0.8437699
## Origin:Treatment2 0.0181 1 0.8928700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g14663.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 25.5 5.73 19 13.5 37.5
## RS 32.8 5.52 19 21.3 44.4
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -7.28 7.13 23 -1.020 0.3184
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g14663_sum<-summarySE(wgcna_counts_filtered_long_g14663 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g14663_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 28.09091 15.23453 4.593384 10.234697
## 2 RF Variable 11 26.54545 14.34129 4.324063 9.634613
## 3 RS Stable 12 33.75000 28.81958 8.319496 18.311087
## 4 RS Variable 12 33.66667 30.09782 8.688492 19.123243Figure
pd<- position_dodge(0.2)
g14663_fig<-ggplot(data=g14663_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Poly~"[ADP-ribose]"~polymerase~11~expression))+
ggtitle(~turquoise)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg14663_fig
***solute carrier family 4 member gamma
wgcna_counts_filtered_long_g15280 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g15280.t1")
wgcna_counts_filtered_long_g15280 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 30 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 34 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 29 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 30 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 94 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 76 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 49 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 39 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 64 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 64 18 D Stable
## # ℹ 36 more rowsg15280.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g15280 , na.action=na.exclude)
car::Anova(g15280.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 86.8426 1 < 2.2e-16 ***
## Origin 8.6455 1 0.003279 **
## Treatment2 0.8180 1 0.365763
## Origin:Treatment2 0.8151 1 0.366627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g15280.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 51.8 4.22 19 43.0 60.7
## RS 32.8 4.04 19 24.3 41.3
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 19 5.84 23 3.255 0.0035
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g15280_sum<-summarySE(wgcna_counts_filtered_long_g15280 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g15280_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 55.63636 24.46333 7.375972 16.43469
## 2 RF Variable 11 48.00000 20.63008 6.220202 13.85947
## 3 RS Stable 12 31.33333 16.77299 4.841946 10.65705
## 4 RS Variable 12 34.25000 16.87454 4.871259 10.72157Figure
pd<- position_dodge(0.2)
g15280_fig<-ggplot(data=g15280_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g15280 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Solute~carrier~family~4~member~gamma~expression))+
ggtitle(~pink)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg15280_fig
MAGUK p55 subfamily member 7-like
wgcna_counts_filtered_long_g15517 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g15517.t1")
wgcna_counts_filtered_long_g15517 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 0 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 14 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 27 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 15 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 12 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 14 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 15 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 0 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 9 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 29 18 D Stable
## # ℹ 36 more rowsg15517.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g15517 , na.action=na.exclude)
car::Anova(g15517.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 27.9449 1 1.248e-07 ***
## Origin 0.2433 1 0.6219
## Treatment2 0.2642 1 0.6073
## Origin:Treatment2 1.3103 1 0.2523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g15517.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 13.5 1.95 19 9.47 17.6
## RS 14.8 1.86 19 10.85 18.6
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -1.2 2.69 23 -0.447 0.6589
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g15517_sum<-summarySE(wgcna_counts_filtered_long_g15517 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g15517_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 14.54545 9.490665 2.861543 6.375916
## 2 RF Variable 11 12.54545 10.103105 3.046201 6.787358
## 3 RS Stable 12 12.66667 7.992421 2.307213 5.078142
## 4 RS Variable 12 16.83333 8.912028 2.572681 5.662432Figure
pd<- position_dodge(0.2)
g15517_fig<-ggplot(data=g15517_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(MAGUK~p55~subfamily~member~7-like~expression))+
ggtitle(~cyan)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg15517_fig
CARP1
wgcna_counts_filtered_long_g16280 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g16280.t1")
wgcna_counts_filtered_long_g16280 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 103 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 165 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 384 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 232 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 309 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 396 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 225 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 228 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 187 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 112 18 D Stable
## # ℹ 36 more rowsg16280.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g16280 , na.action=na.exclude)
car::Anova(g16280.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 68.2145 1 <2e-16 ***
## Origin 0.1913 1 0.6619
## Treatment2 0.0758 1 0.7831
## Origin:Treatment2 0.0179 1 0.8934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g16280.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 276 26.3 19 221 331
## RS 260 25.2 19 207 313
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 16.1 34.7 23 0.463 0.6477
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g16280_sum<-summarySE(wgcna_counts_filtered_long_g16280 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g16280_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 287.2727 124.33752 37.48917 83.53108
## 2 RF Variable 11 275.2727 90.98691 27.43359 61.12584
## 3 RS Stable 12 259.7500 119.95766 34.62879 76.21746
## 4 RS Variable 12 255.8333 115.64824 33.38477 73.47939Figure
pd<- position_dodge(0.2)
g16280_fig<-ggplot(data=g16280_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(CARP1~expression))+
ggtitle(~tan)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg16280_fig
PHD finger protein 21A-like
wgcna_counts_filtered_long_g1634 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g1634.t1")
wgcna_counts_filtered_long_g1634 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 65 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 80 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 90 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 79 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 95 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 95 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 76 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 78 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 91 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 79 18 D Stable
## # ℹ 36 more rowsg1634.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g1634 , na.action=na.exclude)
car::Anova(g1634.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 177.4960 1 <2e-16 ***
## Origin 0.5303 1 0.4665
## Treatment2 0.4221 1 0.5159
## Origin:Treatment2 0.0042 1 0.9480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g1634.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 90.0 4.62 19 80.3 99.7
## RS 96.2 4.42 19 86.9 105.4
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -6.17 6.39 23 -0.965 0.3448
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g1634_sum<-summarySE(wgcna_counts_filtered_long_g1634 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g1634_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 87.00000 11.13553 3.357488 7.48095
## 2 RF Variable 11 93.00000 17.89413 5.395284 12.02144
## 3 RS Stable 12 93.58333 25.61412 7.394161 16.27444
## 4 RS Variable 12 98.75000 27.03911 7.805520 17.17983Figure
pd<- position_dodge(0.2)
g1634_fig<-ggplot(data=g1634_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(PHD~finger~protein~"21A"~like~expression))+
ggtitle(~turquoise)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg1634_fig
digestive cysteine proteinase 1-like
wgcna_counts_filtered_long_g18103 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g18103.t1")
wgcna_counts_filtered_long_g18103 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 0 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 9 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 0 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 12 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 6 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 0 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 19 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 0 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 10 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 10 18 D Stable
## # ℹ 36 more rowsg18103.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g18103 , na.action=na.exclude)
car::Anova(g18103.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 14.6577 1 0.0001289 ***
## Origin 1.8257 1 0.1766413
## Treatment2 0.8843 1 0.3470176
## Origin:Treatment2 0.0057 1 0.9398383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g18103.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 10.14 1.60 19 6.80 13.47
## RS 5.75 1.53 19 2.55 8.95
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 4.39 2.21 23 1.986 0.0590
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g18103_sum<-summarySE(wgcna_counts_filtered_long_g18103 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g18103_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 8.636364 6.313046 1.903455 4.241162
## 2 RF Variable 11 11.636364 10.032674 3.024965 6.740042
## 3 RS Stable 12 4.416667 7.216878 2.083333 4.585386
## 4 RS Variable 12 7.083333 5.822501 1.680811 3.699440Figure
pd<- position_dodge(0.2)
g18103_fig<-ggplot(data=g18103_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g18103 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(digestive~cysteine~proteinase~1-like~expression))+
ggtitle(~blue)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg18103_fig
endothelin-converting enzyme 1-like isoform X2
wgcna_counts_filtered_long_g19211 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g19211.t1")
wgcna_counts_filtered_long_g19211 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 46 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 56 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 51 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 30 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 43 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 27 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 37 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 47 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 54 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 67 18 D Stable
## # ℹ 36 more rowsg19211.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g19211 , na.action=na.exclude)
car::Anova(g19211.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 87.3886 1 < 2e-16 ***
## Origin 3.4868 1 0.06186 .
## Treatment2 0.1819 1 0.66975
## Origin:Treatment2 0.8990 1 0.34305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g19211.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 48.7 3.93 19 40.5 56.9
## RS 57.2 3.77 19 49.3 65.1
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -8.49 5.15 23 -1.647 0.1131
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g19211_sum<-summarySE(wgcna_counts_filtered_long_g19211 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g19211_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 48.18182 12.97550 3.912261 8.717060
## 2 RF Variable 11 50.90909 12.33251 3.718393 8.285096
## 3 RS Stable 12 59.16667 22.09415 6.378032 14.037954
## 4 RS Variable 12 53.50000 17.65065 5.095304 11.214688Figure
pd<- position_dodge(0.2)
g19211_fig<-ggplot(data=g19211_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(endothelin-converting~enzyme~1-like~isoform~X2~expression))+
ggtitle(~green)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg19211_fig
chymotrypsin-like elastase family member 1
wgcna_counts_filtered_long_g19288 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g19288.t1")
wgcna_counts_filtered_long_g19288 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 0 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 0 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 0 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 0 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 0 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 48 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 0 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 25 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 29 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 52 18 D Stable
## # ℹ 36 more rowsg19288.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g19288 , na.action=na.exclude)
car::Anova(g19288.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 6.7903 1 0.009165 **
## Origin 0.0148 1 0.903331
## Treatment2 2.4178 1 0.119960
## Origin:Treatment2 0.0599 1 0.806669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g19288.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 9.58 4.58 19 -0.0019 19.2
## RS 11.54 4.40 19 2.3230 20.8
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -1.96 5.73 23 -0.342 0.7357
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g19288_sum<-summarySE(wgcna_counts_filtered_long_g19288 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g19288_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 13.272727 20.337605 6.132019 13.662989
## 2 RF Variable 11 3.363636 8.834848 2.663807 5.935332
## 3 RS Stable 12 15.833333 25.785244 7.443559 16.383162
## 4 RS Variable 12 8.083333 15.264089 4.406363 9.698340Figure
pd<- position_dodge(0.2)
g19288_fig<-ggplot(data=g19288_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(chymotrypsin-like~elastase~family~member~1~expression))+
ggtitle(~cyan)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg19288_fig
CUB and peptidase domain-containing protein 2-like
wgcna_counts_filtered_long_g21338 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g21338.t1")
wgcna_counts_filtered_long_g21338 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 6 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 0 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 7 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 9 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 25 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 20 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 7 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 0 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 15 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 32 18 D Stable
## # ℹ 36 more rowsg21338.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g21338 , na.action=na.exclude)
car::Anova(g21338.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 19.7037 1 9.043e-06 ***
## Origin 0.8360 1 0.3605
## Treatment2 0.0175 1 0.8947
## Origin:Treatment2 2.2965 1 0.1297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g21338.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 14.3 2.66 19 8.71 19.8
## RS 14.3 2.56 19 8.99 19.7
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -0.0721 3.24 23 -0.022 0.9824
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g21338_sum<-summarySE(wgcna_counts_filtered_long_g21338 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g21338_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 13.00000 11.063453 3.335757 7.432529
## 2 RF Variable 11 13.45455 7.353416 2.217138 4.940092
## 3 RS Stable 12 17.83333 13.354150 3.855011 8.484822
## 4 RS Variable 12 11.08333 10.422514 3.008720 6.622149Figure
pd<- position_dodge(0.2)
g21338_fig<-ggplot(data=g21338_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(CUB~and~peptidase~domain-containing~protein~2-like~expression))+
ggtitle(~cyan)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg21338_fig
L-type calcium channel alpha-1 subunit
wgcna_counts_filtered_long_g21501 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g21501.t1")
wgcna_counts_filtered_long_g21501 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 28 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 29 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 19 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 16 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 31 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 28 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 0 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 23 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 0 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 0 18 D Stable
## # ℹ 36 more rowsg21501.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g21501 , na.action=na.exclude)
car::Anova(g21501.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 33.2008 1 8.312e-09 ***
## Origin 0.5237 1 0.4693
## Treatment2 0.4276 1 0.5132
## Origin:Treatment2 0.1305 1 0.7179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g21501.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 20.3 2.71 19 14.6 26.0
## RS 17.8 2.60 19 12.4 23.3
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 2.48 3.75 23 0.662 0.5145
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g21501_sum<-summarySE(wgcna_counts_filtered_long_g21501 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g21501_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 22.09091 10.02452 3.022505 6.734561
## 2 RF Variable 11 18.54545 12.91792 3.894900 8.678379
## 3 RS Stable 12 18.25000 14.12364 4.077144 8.973734
## 4 RS Variable 12 17.41667 13.22160 3.816746 8.400601Figure
pd<- position_dodge(0.2)
g21501_fig<-ggplot(data=g21501_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(L-type~calcium~channel~alpha-1~subunit~expression))+
ggtitle(~turquoise)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg21501_fig
Protocadherin
wgcna_counts_filtered_long_g21501 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g22388.t1")
wgcna_counts_filtered_long_g21501 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 133 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 138 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 163 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 156 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 164 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 181 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 130 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 185 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 157 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 144 18 D Stable
## # ℹ 36 more rowsg21501.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g21501 , na.action=na.exclude)
car::Anova(g21501.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 302.6581 1 <2e-16 ***
## Origin 1.4899 1 0.2222
## Treatment2 0.7306 1 0.3927
## Origin:Treatment2 0.3360 1 0.5621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g21501.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 162 6.38 19 149 176
## RS 142 6.11 19 129 155
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 20.4 8.83 23 2.306 0.0305
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g21501_sum<-summarySE(wgcna_counts_filtered_long_g21501 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g21501_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 157.0000 20.72679 6.249364 13.92445
## 2 RF Variable 11 167.9091 28.94980 8.728693 19.44874
## 3 RS Stable 12 141.7500 37.05064 10.695599 23.54085
## 4 RS Variable 12 142.4167 29.92250 8.637882 19.01185Figure
pd<- position_dodge(0.2)
g21501_fig<-ggplot(data=g21501_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g21501 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Protocadherin~expression), limits=c(100,200))+
ggtitle(~blue)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg21501_fig## Warning: Removed 5 rows containing missing values (`geom_point()`).
Acropora yongei Na+/Ca2+ exchanger
wgcna_counts_filtered_long_g24639 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g24639.t1")
wgcna_counts_filtered_long_g24639 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 70 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 79 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 66 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 108 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 58 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 94 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 90 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 60 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 62 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 62 18 D Stable
## # ℹ 36 more rowsg24639.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g24639 , na.action=na.exclude)
car::Anova(g24639.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 208.9868 1 <2e-16 ***
## Origin 0.0585 1 0.8089
## Treatment2 0.0158 1 0.8999
## Origin:Treatment2 0.6123 1 0.4339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g24639.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 80.6 4.21 19 71.8 89.4
## RS 78.5 4.03 19 70.1 86.9
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 2.1 5.6 23 0.375 0.7115
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g24639_sum<-summarySE(wgcna_counts_filtered_long_g24639 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g24639_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 81.27273 15.07376 4.544909 10.12669
## 2 RF Variable 11 82.18182 20.54175 6.193572 13.80014
## 3 RS Stable 12 81.58333 16.05365 4.634290 10.20000
## 4 RS Variable 12 74.66667 21.21035 6.122900 13.47641Figure
pd<- position_dodge(0.2)
g24639_fig<-ggplot(data=g24639_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Acropora~yongei~"Na+/Ca2+"~exchanger~expression))+
ggtitle(~tan)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg24639_fig
### **mammalian ependymin-related protein 1-like
wgcna_counts_filtered_long_g25351 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g25351.t1")
wgcna_counts_filtered_long_g25351 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 5677 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 5022 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 13830 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 5729 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 5012 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 8437 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 7397 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 6199 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 5018 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 4565 18 D Stable
## # ℹ 36 more rowsg25351.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g25351 , na.action=na.exclude)
car::Anova(g25351.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 113.2918 1 < 2e-16 ***
## Origin 6.5397 1 0.01055 *
## Treatment2 2.9203 1 0.08747 .
## Origin:Treatment2 1.6792 1 0.19503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g25351.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 6638 396 19 5809 7466
## RS 3944 379 19 3151 4738
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 2693 548 23 4.912 0.0001
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g25351_sum<-summarySE(wgcna_counts_filtered_long_g25351 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g25351_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 5960.909 1716.8516 517.6502 1153.3966
## 2 RF Variable 11 7314.364 2894.5978 872.7541 1944.6172
## 3 RS Stable 12 3978.167 792.8993 228.8903 503.7842
## 4 RS Variable 12 3910.750 1499.1418 432.7650 952.5093Figure
pd<- position_dodge(0.2)
g25351_fig<-ggplot(data=g25351_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g25351,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(mammalian~ependymin-related~protein~1-like~expression), limits=c(2500,12500))+
ggtitle(~blue)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg25351_fig## Warning: Removed 2 rows containing missing values (`geom_point()`).
### MAM and LDLr domain-containing protein
wgcna_counts_filtered_long_g25935 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g25935.t1")
wgcna_counts_filtered_long_g25935 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 1097 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 929 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 1262 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 1089 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 1127 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 1375 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 1184 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 1275 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 1207 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 1525 18 D Stable
## # ℹ 36 more rowsg25935.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g25935 , na.action=na.exclude)
car::Anova(g25935.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 274.9500 1 <2e-16 ***
## Origin 2.0555 1 0.1517
## Treatment2 0.1990 1 0.6555
## Origin:Treatment2 0.4517 1 0.5015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g25935.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 1324 59.6 19 1199 1449
## RS 1216 57.1 19 1096 1335
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 108 81 23 1.338 0.1941
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g25935_sum<-summarySE(wgcna_counts_filtered_long_g25935 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g25935_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 1344.182 302.1115 91.09005 202.9613
## 2 RF Variable 11 1294.818 214.8175 64.76992 144.3164
## 3 RS Stable 12 1183.250 328.6159 94.86323 208.7926
## 4 RS Variable 12 1236.833 214.0076 61.77867 135.9739Figure
pd<- position_dodge(0.2)
g25935_fig<-ggplot(data=g25935_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(MAM~and~LDLr~domain-containing~protein~expression))+
ggtitle(~brown)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg25935_fig
SLIT-ROBO Rho GTPase-activating protein 1-like
wgcna_counts_filtered_long_g27376 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g27376.t1")
wgcna_counts_filtered_long_g27376 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 35 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 28 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 40 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 67 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 19 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 38 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 31 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 39 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 38 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 26 18 D Stable
## # ℹ 36 more rowsg27376.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27376 , na.action=na.exclude)
car::Anova(g27376.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 98.9685 1 <2e-16 ***
## Origin 0.0141 1 0.9054
## Treatment2 0.2753 1 0.5998
## Origin:Treatment2 0.7895 1 0.3742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g27376.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 36.0 2.72 19 30.4 41.7
## RS 38.6 2.60 19 33.2 44.1
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -2.58 3.72 23 -0.694 0.4945
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g27376_sum<-summarySE(wgcna_counts_filtered_long_g27376 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27376_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 37.36364 10.763575 3.245340 7.231068
## 2 RF Variable 11 34.63636 11.209574 3.379814 7.530694
## 3 RS Stable 12 36.83333 16.889121 4.875469 10.730836
## 4 RS Variable 12 40.50000 9.491623 2.739996 6.030690Figure
pd<- position_dodge(0.2)
g27376_fig<-ggplot(data=g27376_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(SLIT-ROBO~Rho~GTPase-activating~protein~1-like~expression))+
ggtitle(~grey60)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg27376_fig
**Hephaestin-like protein - frontloaded
wgcna_counts_filtered_long_g27566 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g27566.t1")
wgcna_counts_filtered_long_g27566 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 12 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 24 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 15 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 21 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 30 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 26 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 15 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 15 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 0 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 17 18 D Stable
## # ℹ 36 more rowsg27566.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27566 , na.action=na.exclude)
car::Anova(g27566.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 56.1260 1 6.797e-14 ***
## Origin 6.3687 1 0.01162 *
## Treatment2 0.0303 1 0.86179
## Origin:Treatment2 0.0252 1 0.87392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g27566.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 19.0 1.83 19 15.22 22.9
## RS 10.4 1.75 19 6.75 14.1
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 8.63 2.53 23 3.410 0.0024
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g27566_sum<-summarySE(wgcna_counts_filtered_long_g27566 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27566_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 19.36364 8.139690 2.454209 5.468318
## 2 RF Variable 11 18.72727 8.978763 2.707199 6.032015
## 3 RS Stable 12 10.33333 9.528267 2.750574 6.053972
## 4 RS Variable 12 10.50000 7.501515 2.165501 4.766235Figure
pd<- position_dodge(0.2)
g27566_fig<-ggplot(data=g27566_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g27566 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Hephaestin-like~protein~expression))+
ggtitle(~brown)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg27566_fig
plasma membrane calcium ATPase - frontloaded
wgcna_counts_filtered_long_g27976 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g27976.t1")
wgcna_counts_filtered_long_g27976 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 197 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 219 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 204 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 124 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 174 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 155 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 183 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 173 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 122 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 223 18 D Stable
## # ℹ 36 more rowsg27976.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27976 , na.action=na.exclude)
car::Anova(g27976.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 209.0712 1 <2e-16 ***
## Origin 0.0008 1 0.9774
## Treatment2 0.3269 1 0.5675
## Origin:Treatment2 0.0012 1 0.9725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g27976.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 184 8.74 19 166 202
## RS 184 8.37 19 166 201
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 0.0682 12.1 23 0.006 0.9956
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g27976_sum<-summarySE(wgcna_counts_filtered_long_g27976 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27976_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 178.8182 39.85177 12.01576 26.77278
## 2 RF Variable 11 188.8182 33.30111 10.04066 22.37199
## 3 RS Stable 12 178.3333 45.44794 13.11969 28.87624
## 4 RS Variable 12 189.1667 43.65950 12.60341 27.73992Figure
pd<- position_dodge(0.2)
g27976_fig<-ggplot(data=g27976_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(plasma~membrane~calcium~ATPase~expression))+
ggtitle(~brown)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg27976_fig
von Willebrand factor D and EGF domain-containing protein-like
wgcna_counts_filtered_long_g28226 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g28226.t2")
wgcna_counts_filtered_long_g28226 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 0 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 0 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 0 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 0 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 66 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 103 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 0 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 0 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 0 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 0 18 D Stable
## # ℹ 36 more rowsg28226.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g28226 , na.action=na.exclude)
car::Anova(g28226.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 13.4863 1 0.0002403 ***
## Origin 6.2703 1 0.0122776 *
## Treatment2 2.3220 1 0.1275576
## Origin:Treatment2 3.0108 1 0.0827128 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g28226.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 30.2 8.97 19 11.43 49.0
## RS 16.6 8.82 19 -1.88 35.1
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 13.6 7.17 23 1.899 0.0702
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g28226_sum<-summarySE(wgcna_counts_filtered_long_g28226 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g28226_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 35.18182 57.46968 17.327759 38.60865
## 2 RF Variable 11 26.36364 40.63317 12.251362 27.29774
## 3 RS Stable 12 13.08333 16.41761 4.739355 10.43125
## 4 RS Variable 12 18.16667 25.95392 7.492252 16.49034Figure
pd<- position_dodge(0.2)
g28226_fig<-ggplot(data=g28226_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g28226,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(von~Willebrand~factor~D~and~EGF~domain-containing~protein-like~expression), limits=c(0,120))+
ggtitle(~magenta)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg28226_fig## Warning: Removed 1 rows containing missing values (`geom_point()`).
low-density lipoprotein receptor-related protein 8-like
wgcna_counts_filtered_long_g4085 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g4085.t1")
wgcna_counts_filtered_long_g4085 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 37 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 42 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 54 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 47 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 54 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 73 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 40 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 31 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 52 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 49 18 D Stable
## # ℹ 36 more rowsg4085.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g4085 , na.action=na.exclude)
car::Anova(g4085.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 247.3507 1 <2e-16 ***
## Origin 0.9727 1 0.3240
## Treatment2 0.8654 1 0.3522
## Origin:Treatment2 2.5023 1 0.1137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g4085.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 45.8 2.15 19 41.3 50.3
## RS 46.4 2.06 19 42.1 50.7
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -0.557 2.98 23 -0.187 0.8532
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g4085_sum<-summarySE(wgcna_counts_filtered_long_g4085 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g4085_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 47.81818 11.151845 3.362408 7.491911
## 2 RF Variable 11 43.81818 8.364427 2.521970 5.619298
## 3 RS Stable 12 43.66667 8.391915 2.422537 5.331969
## 4 RS Variable 12 49.08333 11.881677 3.429945 7.549257Figure
pd<- position_dodge(0.2)
g4085_fig<-ggplot(data=g4085_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(low-density~lipoprotein~receptor-related~protein~8-like~expression))+
ggtitle(~cyan)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg4085_fig
**Cephalotoxin-like protein
wgcna_counts_filtered_long_g5013 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g5013.t1")
wgcna_counts_filtered_long_g5013 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 6 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 14 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 14 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 6 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 23 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 9 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 16 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 21 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 22 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 12 18 D Stable
## # ℹ 36 more rowsg5013.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g5013 , na.action=na.exclude)
car::Anova(g5013.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 44.8432 1 2.135e-11 ***
## Origin 6.0215 1 0.01413 *
## Treatment2 0.2947 1 0.58719
## Origin:Treatment2 0.0002 1 0.99022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g5013.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 13.76 1.41 19 10.80 16.7
## RS 7.22 1.35 19 4.39 10.0
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 6.54 1.93 23 3.393 0.0025
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g5013_sum<-summarySE(wgcna_counts_filtered_long_g5013 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g5013_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 13.090909 7.063350 2.129680 4.745223
## 2 RF Variable 11 14.545455 6.170310 1.860419 4.145271
## 3 RS Stable 12 6.416667 5.583390 1.611786 3.547517
## 4 RS Variable 12 7.916667 6.934215 2.001735 4.405790Figure
pd<- position_dodge(0.2)
g5013_fig<-ggplot(data=g5013_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g5013 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(cephalotoxin-like~expression))+
ggtitle(~blue)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg5013_fig
**sodium bicarbonate cotransporter 3-like isoform X - SLC4A7
wgcna_counts_filtered_long_g7402 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g7402.t1")
wgcna_counts_filtered_long_g7402 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 391 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 785 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 328 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 522 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 799 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 863 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 557 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 533 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 906 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 818 18 D Stable
## # ℹ 36 more rowsg7402.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g7402, na.action=na.exclude)
car::Anova(g7402.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 102.5059 1 < 2e-16 ***
## Origin 6.4414 1 0.01115 *
## Treatment2 1.0268 1 0.31091
## Origin:Treatment2 0.6016 1 0.43798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g7402.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 660 49.6 19 556 764
## RS 467 47.5 19 367 566
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 193 68.7 23 2.814 0.0099
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g7402_sum<-summarySE(wgcna_counts_filtered_long_g7402 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g7402_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 710.3636 265.8632 80.16078 178.6093
## 2 RF Variable 11 609.8182 241.3930 72.78272 162.1700
## 3 RS Stable 12 463.8333 248.6454 71.77773 157.9817
## 4 RS Variable 12 469.8333 166.4407 48.04730 105.7514Figure
pd<- position_dodge(0.2)
g7402_fig<-ggplot(data=g7402_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g7402 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(sodium~bicarbonate~cotransporter~3-like~isoform~X2~expression))+
ggtitle(~pink)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg7402_fig
*protein lingerer-like
wgcna_counts_filtered_long_g7902 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g7908.t1")
wgcna_counts_filtered_long_g7902 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 51 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 73 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 101 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 79 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 107 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 95 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 61 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 75 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 92 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 68 18 D Stable
## # ℹ 36 more rowsg7902.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g7902, na.action=na.exclude)
car::Anova(g7902.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 167.9194 1 < 2.2e-16 ***
## Origin 11.0673 1 0.0008786 ***
## Treatment2 0.0009 1 0.9759807
## Origin:Treatment2 0.0806 1 0.7764657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g7902.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 82.9 4.53 19 73.4 92.3
## RS 51.6 4.34 19 42.5 60.7
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 31.3 6.27 23 4.989 <.0001
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g7902_sum<-summarySE(wgcna_counts_filtered_long_g7902 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g7902_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 83.00000 24.55606 7.403930 16.49698
## 2 RF Variable 11 82.72727 21.99587 6.632004 14.77702
## 3 RS Stable 12 53.50000 19.52853 5.637402 12.40784
## 4 RS Variable 12 49.66667 18.80683 5.429065 11.94929Figure
pd<- position_dodge(0.2)
g7902_fig<-ggplot(data=g7902_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_g7902,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(protein~lingerer-like~expression))+
ggtitle(~pink)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg7902_fig
***CA1
wgcna_counts_filtered_long_CA1<- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g12304.t1")
wgcna_counts_filtered_long_CA1## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 2854 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 4635 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 2949 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 4681 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 7704 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 8665 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 3948 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 3887 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 6896 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 6597 18 D Stable
## # ℹ 36 more rowsCA1.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_CA1, na.action=na.exclude)
car::Anova(CA1.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 97.1566 1 < 2.2e-16 ***
## Origin 15.7334 1 7.293e-05 ***
## Treatment2 2.1565 1 0.142
## Origin:Treatment2 1.3573 1 0.244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(CA1.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 5346 434 19 4437 6256
## RS 2716 416 19 1846 3587
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 2630 597 23 4.408 0.0002
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
CA1_sum<-summarySE(wgcna_counts_filtered_long_CA1, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA1_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 5970.636 2621.084 790.2864 1760.8679
## 2 RF Variable 11 4733.455 1986.062 598.8204 1334.2549
## 3 RS Stable 12 2653.583 1847.177 533.2340 1173.6400
## 4 RS Variable 12 2775.250 1463.636 422.5154 929.9501Figure
pd<- position_dodge(0.2)
CA1_fig<-ggplot(data=CA1_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
geom_point(data=wgcna_counts_filtered_long_CA1,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(CA1~expression))+
ggtitle(~pink)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statCA1_fig
Uncharacterized skeletal organic matrix protein-6 (USOMP6)
wgcna_counts_filtered_long_g22622 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g22622.t1")
wgcna_counts_filtered_long_g22622 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 902 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 733 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 1136 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 857 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 558 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 726 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 774 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 888 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 617 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 1117 18 D Stable
## # ℹ 36 more rowsg22622.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g22622, na.action=na.exclude)
car::Anova(g22622.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 155.9337 1 <2e-16 ***
## Origin 0.0013 1 0.9712
## Treatment2 0.5378 1 0.4633
## Origin:Treatment2 0.7739 1 0.3790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g22622.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 903 54.2 19 790 1016
## RS 846 51.9 19 738 955
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 56.9 71 23 0.801 0.4313
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g22622_sum<-summarySE(wgcna_counts_filtered_long_g22622 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g22622_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 870.9091 172.5407 52.02298 115.9144
## 2 RF Variable 11 935.3636 251.5207 75.83634 168.9739
## 3 RS Stable 12 859.9167 294.4059 84.98765 187.0566
## 4 RS Variable 12 817.3333 190.8728 55.10023 121.2748Figure
pd<- position_dodge(0.2)
g22622_fig<-ggplot(data=g22622_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Uncharacterized~skeletal~organic~matrix~protein-6~(USOMP6)~expression))+
ggtitle(~green)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg22622_fig
band 3 anion transport protein-like
wgcna_counts_filtered_long_g27873 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g27873.t1")
wgcna_counts_filtered_long_g27873 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 52 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 65 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 105 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 52 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 22 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 50 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 44 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 94 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 58 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 60 18 D Stable
## # ℹ 36 more rowsg27873.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27873, na.action=na.exclude)
car::Anova(g27873.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 109.0383 1 <2e-16 ***
## Origin 0.7687 1 0.3806
## Treatment2 0.0156 1 0.9006
## Origin:Treatment2 0.2677 1 0.6049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g27873.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 71.1 5.15 19 60.3 81.8
## RS 59.9 4.94 19 49.6 70.3
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 11.1 6.84 23 1.628 0.1171
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g27873_sum<-summarySE(wgcna_counts_filtered_long_g27873 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27873_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 70.63636 21.65767 6.530032 14.54982
## 2 RF Variable 11 71.72727 27.65896 8.339491 18.58154
## 3 RS Stable 12 61.50000 22.71763 6.558016 14.43410
## 4 RS Variable 12 56.33333 17.55166 5.066726 11.15179Figure
pd<- position_dodge(0.2)
g27873_fig<-ggplot(data=g27873_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(band~3~anion~transport~protein-like~expression))+
ggtitle(~green)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg27873_fig
collagenase 3-like
wgcna_counts_filtered_long_g5338 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g5338.t1")
wgcna_counts_filtered_long_g5338 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 15 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 14 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 9 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 35 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 22 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 16 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 24 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 30 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 21 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 22 18 D Stable
## # ℹ 36 more rowsg5338.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g5338, na.action=na.exclude)
car::Anova(g5338.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 64.2427 1 1.1e-15 ***
## Origin 0.7901 1 0.3741
## Treatment2 0.0652 1 0.7985
## Origin:Treatment2 0.0281 1 0.8670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g5338.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 20.6 1.78 19 16.9 24.4
## RS 17.1 1.70 19 13.6 20.7
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS 3.51 2.46 23 1.425 0.1677
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g5338_sum<-summarySE(wgcna_counts_filtered_long_g5338 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g5338_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 20.18182 7.180782 2.165087 4.824115
## 2 RF Variable 11 21.09091 7.955558 2.398691 5.344617
## 3 RS Stable 12 17.08333 10.202124 2.945100 6.482120
## 4 RS Variable 12 17.16667 7.601834 2.194460 4.829975Figure
pd<- position_dodge(0.2)
g5338_fig<-ggplot(data=g5338_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(collagenase~3-like~expression))+
ggtitle(~green)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg5338_fig
Protocadherin
wgcna_counts_filtered_long_g6583 <- wgcna_counts_filtered_long %>%
filter(Gene == "Pocillopora_acuta_HIv2___TS.g6583.t1")
wgcna_counts_filtered_long_g6583 ## # A tibble: 46 × 7
## Gene Origin Colony.number Counts Colony Treatment Treatment2
## <chr> <fct> <chr> <int> <dbl> <fct> <fct>
## 1 Pocillopora_acuta_HI… RF 13B 254 13 B Variable
## 2 Pocillopora_acuta_HI… RF 13D 287 13 D Stable
## 3 Pocillopora_acuta_HI… RF 14B 305 14 B Variable
## 4 Pocillopora_acuta_HI… RF 14C 229 14 C Stable
## 5 Pocillopora_acuta_HI… RF 15B 320 15 B Variable
## 6 Pocillopora_acuta_HI… RF 15D 241 15 D Stable
## 7 Pocillopora_acuta_HI… RF 17B 171 17 B Variable
## 8 Pocillopora_acuta_HI… RF 17D 261 17 D Stable
## 9 Pocillopora_acuta_HI… RF 18B 256 18 B Variable
## 10 Pocillopora_acuta_HI… RF 18D 274 18 D Stable
## # ℹ 36 more rowsg6583.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g6583, na.action=na.exclude)
car::Anova(g6583.lme, type=3)## Analysis of Deviance Table (Type III tests)
##
## Response: Counts
## Chisq Df Pr(>Chisq)
## (Intercept) 245.3182 1 <2e-16 ***
## Origin 1.5732 1 0.2097
## Treatment2 0.0685 1 0.7935
## Origin:Treatment2 0.5712 1 0.4498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tukey3<- emmeans(g6583.lme, list(pairwise ~ Origin), adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionstukey3## $`emmeans of Origin`
## Origin emmean SE df lower.CL upper.CL
## RF 283 13.2 19 256 311
## RS 302 12.6 19 275 328
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containment
## Confidence level used: 0.95
##
## $`pairwise differences of Origin`
## 1 estimate SE df t.ratio p.value
## RF - RS -18.3 18.1 23 -1.014 0.3210
##
## Results are averaged over the levels of: Treatment2
## Degrees-of-freedom method: containmentlibrary(Rmisc)
g6583_sum<-summarySE(wgcna_counts_filtered_long_g6583 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g6583_sum## Origin Treatment2 N Counts sd se ci
## 1 RF Stable 11 286.9091 43.36463 13.07493 29.13275
## 2 RF Variable 11 280.2727 48.05224 14.48830 32.28194
## 3 RS Stable 12 318.0000 80.29491 23.17914 51.01695
## 4 RS Variable 12 284.8333 61.77648 17.83333 39.25090Figure
pd<- position_dodge(0.2)
g6583_fig<-ggplot(data=g6583_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
#geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
geom_point(size=3, stat="identity", position = pd)+
geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
scale_y_continuous(expression(Protocadherin~expression))+
ggtitle(~black)+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
#strip.background = element_blank(),
#strip.text = element_blank(),
legend.title = element_text(vjust=0.5,size=12),
legend.position="none",
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_text(size=12))#making the axis title larger ## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: statg6583_fig
Comparison of sig genes
biomin_compare_figs<-cowplot::plot_grid(g10093_fig,CA2_fig,g25351_fig,g5013_fig,g15280_fig, CA1_fig,g7402_fig,g7902_fig,g27566_fig,g28226_fig,g14505_fig,g11609_fig,g10093_fig, nrow=4)## Warning: Removed 2 rows containing missing values (`geom_point()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).biomin_compare_figs
Merge the frequency of GOterms for both up- and down-regulation of calcification
The following list is a list of BP-ontology (“Biological Process”) GO terms that were significantly enriched (P_adj < 0.00001) within the modules that were upregulated for calcification. The list has been further reduced by using the package rrvgo.
cal_up_terms <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")
cal_up_terms <- cal_up_terms %>% mutate(Factor = "Up")
head(cal_up_terms)## X.1 X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 1 GO:0006807 0 1 1215
## 2 2 2 GO:0007275 0 1 978
## 3 3 4 GO:0008152 0 1 1387
## 4 4 5 GO:0009987 0 1 1938
## 5 5 6 GO:0016043 0 1 1049
## 6 6 7 GO:0019222 0 1 982
## numInCat term ontology bh_adjust
## 1 1215 nitrogen compound metabolic process BP 0
## 2 978 multicellular organism development BP 0
## 3 1387 metabolic process BP 0
## 4 1938 cellular process BP 0
## 5 1049 cellular component organization BP 0
## 6 982 regulation of metabolic process BP 0
## ParentTerm Factor
## 1 metabolic process Up
## 2 developmental process Up
## 3 metabolic process Up
## 4 cellular process Up
## 5 cellular component organization or biogenesis Up
## 6 regulation of metabolic process UpThe following list is a list of BP-ontology (“Biological Process”) GO terms that were significantly enriched (P_adj < 0.00001) within the modules that were downregulated for calcification. The list has been further reduced by using the package rrvgo.
cal_down_terms <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")
cal_down_terms<-cal_down_terms %>% mutate(Factor = "Down")
head(cal_down_terms)## X.1 X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 1 GO:0006807 0 1 910
## 2 2 2 GO:0007275 0 1 610
## 3 3 4 GO:0008152 0 1 998
## 4 4 5 GO:0009987 0 1 1277
## 5 5 6 GO:0016043 0 1 652
## 6 6 7 GO:0019222 0 1 599
## numInCat term ontology bh_adjust
## 1 910 nitrogen compound metabolic process BP 0
## 2 610 multicellular organism development BP 0
## 3 998 metabolic process BP 0
## 4 1277 cellular process BP 0
## 5 652 cellular component organization BP 0
## 6 599 regulation of metabolic process BP 0
## ParentTerm Factor
## 1 metabolic process Down
## 2 developmental process Down
## 3 metabolic process Down
## 4 cellular process Down
## 5 cellular component organization or biogenesis Down
## 6 regulation of metabolic process DownMerge up and down-regulation of calcification GOterms
The following list is a list of BP-ontology (“Biological Process”) GO terms that were significantly enriched (P < 0.0001) within the modules that were upregulated or downregulated for calcification. The list has been further reduced by using the package rrvgo.
all_terms <- rbind(cal_down_terms,cal_up_terms)
all_terms <- all_terms[,c("Factor","GOterm","over_represented_pvalue","under_represented_pvalue","numDEInCat","numInCat","term","ontology","bh_adjust","ParentTerm")]
all_terms$GOterm<-as.factor(all_terms$GOterm)
dim(all_terms) #3273 reduced terms ## [1] 3273 10length(unique(all_terms$GOterm)) #this represents 1809 unique terms between the two lists of reduced terms## [1] 1809length(unique(all_terms$ParentTerm)) #this represents 139 unique terms between the two lists of reduced terms## [1] 139Unique terms
This is collapsing the list from 3273 rows to only 1809, representing the 1809 unique GO terms in the list above. It collapses the information in the columns “ParentTerm” and “Factor” so that for each GO term we get the parent terms associated and if this was enriched in modules upregulated for calcification, downregulated for calcification, or both.
goterms_shared <- all_terms %>%
group_by(GOterm) %>%
dplyr::summarise(
ParentTerm = paste(unique(ParentTerm), collapse = ", "),
Factor = paste(unique(Factor), collapse = ", "),
term = paste(unique(term), collapse = ", ")
)
dim(goterms_shared)## [1] 1809 4goterms_shared_full <- goterms_shared %>%
left_join(dplyr::select(all_terms,GOterm, Factor, bh_adjust), by = "GOterm") %>% #select 3 columns GOterm, Factor, bh_adjust from all_terms and left join by GOterm, turns this dataframe from 1809 rows back to the 3273 in all_terms
mutate(bh_adjust_Up = ifelse(Factor.y == "Up", bh_adjust, NA)) %>% #add a column that is the p-value for the Up factor
mutate(bh_adjust_Down = ifelse(Factor.y == "Down", bh_adjust, NA)) %>% #add a column that is the p-value for the Down factor
dplyr::select(-c("bh_adjust", "Factor.y")) %>% #remove the unique columns so that we can collapse the dataframe
group_by(GOterm, ParentTerm, Factor.x, term) %>% #group by the repeated columns for the non-unique GO terms
dplyr::summarize(bh_adjust_Up = na.omit(bh_adjust_Up)[1], #carry over the p-value for the term in the up direction, by taking the first non-NA value.
bh_adjust_Down = na.omit(bh_adjust_Down)[1]) %>% #carry over the p-value for the term in the down direction, by taking the first non-NA value.
rename(Factor.x = "Factor") #rename column ## `summarise()` has grouped output by 'GOterm', 'ParentTerm', 'Factor.x'. You can
## override using the `.groups` argument.write.csv(goterms_shared_full, "../../output/WGCNA/GO_analysis/Merged_GOterms_factor_ParentTerm.csv")GOSlim
I downloaded the GOSlim terms list from the following website on 4/5/2024: http://www.informatics.jax.org/gotools/data/input/map2MGIslim.txt
I did so by going to this link and right clicking “here” at “Tab-delimited file mapping GO_ids to MGI GO_Slim category available for download here” and saying “Save link as…” , and I saved it as “map2MGIslim.txt”. I then converted this to CSV and uploaded to this github repository here.
Add slim terms to dataset
GOslim <- read.csv("../../data/map2MGIslim.csv") %>% dplyr::select(-c(term,aspect))goterms_shared_full_slim <- goterms_shared_full %>%
inner_join(GOslim, by = c("GOterm" = "GO_id"))
goterms_up_slim <- goterms_shared_full_slim %>% dplyr::filter(Factor=="Up" | Factor=="Down, Up")
goterms_up_full_slim_plot <- goterms_up_slim %>%
ggplot(aes(x=bh_adjust_Up,
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Over-represented p-value", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text.y = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
); goterms_up_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Up_GOSlim.pdf", goterms_up_full_slim_plot, width = 5, height = 35)
goterms_up_full_slim_plot <- goterms_up_slim %>%
ggplot(aes(x=log10(bh_adjust_Up),
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Log 10(Over-represented p-value)", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text.y = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
); goterms_up_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Up_GOSlim_Log.pdf", goterms_up_full_slim_plot, width = 5, height = 35)goterms_down_slim <- goterms_shared_full_slim %>% dplyr::filter(Factor=="Down" | Factor=="Down, Up")
goterms_down_full_slim_plot <- goterms_down_slim %>%
ggplot(aes(x=bh_adjust_Down,
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Over-represented p-value", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
legend.text=element_text(size = 5),
legend.title = element_text(size = 5)
); goterms_down_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Down_GOSlim.pdf", goterms_down_full_slim_plot, width = 5, height = 35)
goterms_down_full_slim_plot <- goterms_down_slim %>%
ggplot(aes(x=log10(bh_adjust_Down),
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Log 10(Over-represented p-value)", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
legend.text=element_text(size = 5),
legend.title = element_text(size = 5)
); goterms_down_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Down_GOSlim_Log.pdf", goterms_down_full_slim_plot, width = 5, height = 35)For each parent term in this list, how many are “Up” by calcification, “Down”, or both
SharedGOterms = # of GO terms within the parent term that are in the list for “Down”, “Up”, or “Down, Up”
result_parent_unique <- goterms_shared %>%
group_by(ParentTerm,Factor) %>%
dplyr::summarise(SharedGOterms = n_distinct(GOterm)) %>%
arrange(-SharedGOterms)## `summarise()` has grouped output by 'ParentTerm'. You can override using the
## `.groups` argument.parent_filtered_up <- result_parent_unique %>%
dplyr::filter(Factor=="Up" | Factor=="Down, Up")
#dplyr::filter(SharedGOterms>=5) parent_filtered_down <- result_parent_unique %>%
dplyr::filter(Factor=="Down" | Factor=="Down, Up")
#dplyr::filter(SharedGOterms>=5) Count number of GOterms by ParentTerm for the upregulation of calcification
result_up <- cal_up_terms %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Number.of.terms = n_distinct(term))%>%
mutate(Calcification.direction = "Up")
head(result_up)## # A tibble: 6 × 3
## ParentTerm Number.of.terms Calcification.direction
## <chr> <int> <chr>
## 1 DNA metabolic process 16 Up
## 2 RNA processing 28 Up
## 3 actin filament-based process 12 Up
## 4 aging 2 Up
## 5 amide metabolic process 12 Up
## 6 ammonium ion metabolic process 1 Updim(result_up)## [1] 130 3merged_up <- parent_filtered_up %>%
tidyr::separate_rows(ParentTerm, sep = ", ") %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Sum_of_SharedGOterms = sum(SharedGOterms, na.rm = TRUE)) %>%
left_join(result_up, by = "ParentTerm") #join with result from above to get the Number.of.Terms column
merged_up_clean <- na.omit(merged_up)
head(merged_up_clean)## # A tibble: 6 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 DNA metabolic pro… 16 16 Up
## 2 RNA processing 28 28 Up
## 3 actin filament-ba… 12 12 Up
## 4 aging 2 2 Up
## 5 amide metabolic p… 12 12 Up
## 6 ammonium ion meta… 1 1 Up
## # ℹ abbreviated name: ¹Calcification.directiondim(merged_up_clean)## [1] 130 4Count number of GOterms by ParentTerm for the downregulation of calcification
result_down <- cal_down_terms %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Number.of.terms = n_distinct(term))%>%
mutate(Calcification.direction = "Down")
dim(result_down)## [1] 127 3head(result_down)## # A tibble: 6 × 3
## ParentTerm Number.of.terms Calcification.direction
## <chr> <int> <chr>
## 1 RNA processing 28 Down
## 2 actin filament-based process 10 Down
## 3 aging 2 Down
## 4 amide metabolic process 12 Down
## 5 ammonium ion metabolic process 1 Down
## 6 anatomical structure morphogenesis 23 Downmerged_down <- parent_filtered_down %>%
tidyr::separate_rows(ParentTerm, sep = ", ") %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Sum_of_SharedGOterms = sum(SharedGOterms, na.rm = TRUE)) %>%
left_join(result_down, by = "ParentTerm")
merged_down_clean<- na.omit(merged_down)
head(merged_down_clean)## # A tibble: 6 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 RNA processing 28 28 Down
## 2 actin filament-ba… 10 10 Down
## 3 aging 2 2 Down
## 4 amide metabolic p… 12 12 Down
## 5 ammonium ion meta… 1 1 Down
## 6 anatomical struct… 29 23 Down
## # ℹ abbreviated name: ¹Calcification.directionFrequency >10 up
cal_freq_terms_filtered_up <- merged_up_clean %>%
filter(Number.of.terms>=10) %>%
filter(Calcification.direction=="Up")
dim(cal_freq_terms_filtered_up)## [1] 69 4Figure
#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), group=1))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.freq_fig_up
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up.png", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up.pdf", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageFrequency >10 down
cal_freq_terms_filtered_down <- merged_down_clean %>%
filter(Number.of.terms>=10)
#filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down## # A tibble: 61 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 RNA processing 28 28 Down
## 2 actin filament-b… 10 10 Down
## 3 amide metabolic … 12 12 Down
## 4 anatomical struc… 29 23 Down
## 5 animal organ dev… 16 16 Down
## 6 biosynthetic pro… 24 24 Down
## 7 carbohydrate der… 17 17 Down
## 8 carbohydrate met… 12 12 Down
## 9 catabolic process 39 39 Down
## 10 cell activation 13 13 Down
## # ℹ 51 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
freq_fig_down<-ggplot(cal_freq_terms_filtered_down, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms)))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
#geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+
#scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_down
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down.png", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down.pdf", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imagecompare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, nrow=2, align="v")
compare_figs
Frequency >20 up
cal_freq_terms_filtered_up <- merged_up_clean %>%
filter(Number.of.terms>=20) %>%
filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up## # A tibble: 28 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 RNA processing 28 28 Up
## 2 anatomical struc… 28 28 Up
## 3 biosynthetic pro… 20 20 Up
## 4 carbohydrate der… 23 22 Up
## 5 catabolic process 38 36 Up
## 6 cell cycle 51 50 Up
## 7 cell projection … 27 27 Up
## 8 cell surface rec… 22 22 Up
## 9 cellular compone… 31 31 Up
## 10 cellular localiz… 31 31 Up
## # ℹ 18 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms),group=1))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_up
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_gr20.png", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_gr20.pdf", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageFrequency >20 down
cal_freq_terms_filtered_down <- merged_down_clean %>%
filter(Number.of.terms>=20)
#filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down## # A tibble: 29 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 RNA processing 28 28 Down
## 2 anatomical struc… 29 23 Down
## 3 biosynthetic pro… 24 24 Down
## 4 catabolic process 39 39 Down
## 5 cell cycle 41 41 Down
## 6 cell projection … 20 20 Down
## 7 cellular compone… 24 24 Down
## 8 cellular compone… 27 26 Down
## 9 cellular localiz… 28 24 Down
## 10 cellular macromo… 21 21 Down
## # ℹ 19 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
freq_fig_down<-ggplot(cal_freq_terms_filtered_down, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms)))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
#geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+
#scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_down
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_gr20.png", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_gr20.pdf", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imagecompare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, nrow=2, align="v")
compare_figs
All terms up
cal_freq_terms_filtered_up_all <- merged_up_clean %>%
filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up_all## # A tibble: 130 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 DNA metabolic pr… 16 16 Up
## 2 RNA processing 28 28 Up
## 3 actin filament-b… 12 12 Up
## 4 aging 2 2 Up
## 5 amide metabolic … 12 12 Up
## 6 ammonium ion met… 1 1 Up
## 7 anatomical struc… 28 28 Up
## 8 animal organ dev… 16 16 Up
## 9 behavior 8 8 Up
## 10 biological proce… 5 5 Up
## # ℹ 120 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up_all, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms),group=1))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_up
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_ALL.png", plot=freq_fig_up, dpi=300, height=17, units="in", limitsize=FALSE)## Saving 7 x 17 in imageggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_ALL.pdf", plot=freq_fig_up, dpi=300, height=17, units="in", limitsize=FALSE)## Saving 7 x 17 in imageAll terms down
cal_freq_terms_filtered_down_all <- merged_down_clean %>%
filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down_all## # A tibble: 127 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 RNA processing 28 28 Down
## 2 actin filament-b… 10 10 Down
## 3 aging 2 2 Down
## 4 amide metabolic … 12 12 Down
## 5 ammonium ion met… 1 1 Down
## 6 anatomical struc… 29 23 Down
## 7 animal organ dev… 16 16 Down
## 8 behavior 8 8 Down
## 9 biological proce… 6 6 Down
## 10 biosynthetic pro… 24 24 Down
## # ℹ 117 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
freq_fig_down<-ggplot(cal_freq_terms_filtered_down_all, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms)))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
#geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+
#scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_down
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_ALL.png", plot=freq_fig_down, dpi=300, height=17, units="in", limitsize=FALSE)## Saving 7 x 17 in imageggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_ALL.pdf", plot=freq_fig_down, dpi=300, height=17, units="in", limitsize=FALSE)## Saving 7 x 17 in imagecompare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, ncol=2, align="h")
compare_figs